Overview

Dataset statistics

Number of variables88
Number of observations1190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory818.3 KiB
Average record size in memory704.1 B

Variable types

Numeric4
Categorical84

Alerts

ZSN_A has constant value "0.0"Constant
endocr_03 has constant value "0.0"Constant
post_im has constant value "0.0"Constant
R_AB_1_n has constant value "0.0"Constant
R_AB_2_n has constant value "0.0"Constant
R_AB_3_n has constant value "0.0"Constant
NA_R_2_n has constant value "0.0"Constant
NA_R_3_n has constant value "0.0"Constant
NOT_NA_2_n has constant value "0.0"Constant
NOT_NA_3_n has constant value "0.0"Constant
FK_STENOK is highly overall correlated with IBS_POST and 1 other fieldsHigh correlation
IBS_POST is highly overall correlated with FK_STENOKHigh correlation
STENOK_AN is highly overall correlated with FK_STENOKHigh correlation
SVT_POST is highly overall correlated with n_r_ecg_p_08High correlation
n_r_ecg_p_06 is highly overall correlated with nr_04High correlation
n_r_ecg_p_08 is highly overall correlated with SVT_POSTHigh correlation
nr_04 is highly overall correlated with n_r_ecg_p_06High correlation
SIM_GIPERT is highly imbalanced (79.2%)Imbalance
nr_11 is highly imbalanced (81.3%)Imbalance
nr_01 is highly imbalanced (98.2%)Imbalance
nr_02 is highly imbalanced (90.2%)Imbalance
nr_03 is highly imbalanced (87.2%)Imbalance
nr_04 is highly imbalanced (90.8%)Imbalance
nr_07 is highly imbalanced (99.0%)Imbalance
nr_08 is highly imbalanced (98.2%)Imbalance
np_01 is highly imbalanced (99.0%)Imbalance
np_04 is highly imbalanced (98.2%)Imbalance
np_05 is highly imbalanced (93.0%)Imbalance
np_07 is highly imbalanced (99.0%)Imbalance
np_08 is highly imbalanced (97.5%)Imbalance
np_09 is highly imbalanced (98.2%)Imbalance
np_10 is highly imbalanced (99.0%)Imbalance
endocr_02 is highly imbalanced (86.7%)Imbalance
zab_leg_01 is highly imbalanced (52.3%)Imbalance
zab_leg_02 is highly imbalanced (71.9%)Imbalance
zab_leg_03 is highly imbalanced (90.8%)Imbalance
zab_leg_04 is highly imbalanced (97.5%)Imbalance
zab_leg_06 is highly imbalanced (88.2%)Imbalance
O_L_POST is highly imbalanced (74.1%)Imbalance
K_SH_POST is highly imbalanced (98.2%)Imbalance
MP_TP_POST is highly imbalanced (73.7%)Imbalance
SVT_POST is highly imbalanced (95.4%)Imbalance
GT_POST is highly imbalanced (96.1%)Imbalance
FIB_G_POST is highly imbalanced (94.8%)Imbalance
IM_PG_P is highly imbalanced (85.8%)Imbalance
n_r_ecg_p_01 is highly imbalanced (78.4%)Imbalance
n_r_ecg_p_02 is highly imbalanced (96.1%)Imbalance
n_r_ecg_p_04 is highly imbalanced (75.2%)Imbalance
n_r_ecg_p_05 is highly imbalanced (83.0%)Imbalance
n_r_ecg_p_06 is highly imbalanced (89.7%)Imbalance
n_r_ecg_p_08 is highly imbalanced (97.5%)Imbalance
n_r_ecg_p_09 is highly imbalanced (99.0%)Imbalance
n_r_ecg_p_10 is highly imbalanced (98.2%)Imbalance
n_p_ecg_p_01 is highly imbalanced (98.2%)Imbalance
n_p_ecg_p_03 is highly imbalanced (89.7%)Imbalance
n_p_ecg_p_04 is highly imbalanced (98.2%)Imbalance
n_p_ecg_p_05 is highly imbalanced (99.0%)Imbalance
n_p_ecg_p_06 is highly imbalanced (94.2%)Imbalance
n_p_ecg_p_07 is highly imbalanced (68.4%)Imbalance
n_p_ecg_p_08 is highly imbalanced (96.8%)Imbalance
n_p_ecg_p_09 is highly imbalanced (94.2%)Imbalance
n_p_ecg_p_10 is highly imbalanced (88.7%)Imbalance
n_p_ecg_p_11 is highly imbalanced (86.7%)Imbalance
n_p_ecg_p_12 is highly imbalanced (79.2%)Imbalance
fibr_ter_01 is highly imbalanced (93.0%)Imbalance
fibr_ter_02 is highly imbalanced (95.4%)Imbalance
fibr_ter_03 is highly imbalanced (78.0%)Imbalance
fibr_ter_05 is highly imbalanced (97.5%)Imbalance
fibr_ter_06 is highly imbalanced (95.4%)Imbalance
fibr_ter_07 is highly imbalanced (96.1%)Imbalance
fibr_ter_08 is highly imbalanced (99.0%)Imbalance
NITR_S is highly imbalanced (65.1%)Imbalance
TIKL_S_n is highly imbalanced (85.3%)Imbalance
L_BLOOD has 71 (6.0%) zerosZeros

Reproduction

Analysis started2024-05-30 10:22:40.104578
Analysis finished2024-05-30 10:23:01.550205
Duration21.45 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

AGE
Real number (ℝ)

Distinct60
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.865921 × 10-16
Minimum-2.6718422
Maximum2.7062007
Zeros3
Zeros (%)0.3%
Negative561
Negative (%)47.1%
Memory size9.4 KiB
2024-05-30T17:23:01.707150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2.6718422
5-th percentile-1.7306847
Q1-0.65507613
median0.061996254
Q30.68943459
95-th percentile1.5857751
Maximum2.7062007
Range5.3780429
Interquartile range (IQR)1.3445107

Descriptive statistics

Standard deviation1.0004204
Coefficient of variation (CV)-5.3615368 × 1015
Kurtosis-0.25072477
Mean-1.865921 × 10-16
Median Absolute Deviation (MAD)0.71707238
Skewness-0.1380738
Sum-2.2026825 × 10-13
Variance1.000841
MonotonicityNot monotonic
2024-05-30T17:23:01.906621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2412643498 61
 
5.1%
0.1516303019 59
 
5.0%
0.4205324458 50
 
4.2%
-0.7447101778 47
 
3.9%
0.3308983978 47
 
3.9%
0.8687026856 43
 
3.6%
0.0619962539 39
 
3.3%
-0.4758080339 38
 
3.2%
-0.296539938 37
 
3.1%
-0.117271842 36
 
3.0%
Other values (50) 733
61.6%
ValueCountFrequency (%)
-2.671842209 4
 
0.3%
-2.537391137 3
 
0.3%
-2.447757089 2
 
0.2%
-2.358123041 4
 
0.3%
-2.268488993 5
 
0.4%
-2.178854945 2
 
0.2%
-2.089220897 13
1.1%
-1.999586849 10
0.8%
-1.909952801 4
 
0.3%
-1.820318753 6
0.5%
ValueCountFrequency (%)
2.706200669 2
 
0.2%
2.482115549 4
 
0.3%
2.392481501 2
 
0.2%
2.302847453 1
 
0.1%
2.213213405 1
 
0.1%
2.123579357 3
 
0.3%
2.033945309 10
0.8%
1.944311261 4
 
0.3%
1.854677213 7
0.6%
1.765043165 11
0.9%

SEX
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size87.7 KiB
0.7088901554720962
792 
-1.4106557867685932
398 

Length

Max length19
Median length18
Mean length18.334454
Min length18

Characters and Unicode

Total characters21818
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.7088901554720962
2nd row0.7088901554720962
3rd row0.7088901554720962
4th row-1.4106557867685932
5th row0.7088901554720962

Common Values

ValueCountFrequency (%)
0.7088901554720962 792
66.6%
-1.4106557867685932 398
33.4%

Length

2024-05-30T17:23:02.074934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:02.198372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.7088901554720962 792
66.6%
1.4106557867685932 398
33.4%

Most occurring characters

ValueCountFrequency (%)
0 3566
16.3%
5 2778
12.7%
7 2380
10.9%
8 2380
10.9%
6 1986
9.1%
9 1982
9.1%
2 1982
9.1%
1 1588
7.3%
. 1190
 
5.5%
4 1190
 
5.5%
Other values (2) 796
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21818
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3566
16.3%
5 2778
12.7%
7 2380
10.9%
8 2380
10.9%
6 1986
9.1%
9 1982
9.1%
2 1982
9.1%
1 1588
7.3%
. 1190
 
5.5%
4 1190
 
5.5%
Other values (2) 796
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21818
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3566
16.3%
5 2778
12.7%
7 2380
10.9%
8 2380
10.9%
6 1986
9.1%
9 1982
9.1%
2 1982
9.1%
1 1588
7.3%
. 1190
 
5.5%
4 1190
 
5.5%
Other values (2) 796
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21818
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3566
16.3%
5 2778
12.7%
7 2380
10.9%
8 2380
10.9%
6 1986
9.1%
9 1982
9.1%
2 1982
9.1%
1 1588
7.3%
. 1190
 
5.5%
4 1190
 
5.5%
Other values (2) 796
 
3.6%

INF_ANAM
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size88.0 KiB
-0.6491580568765987
778 
0.6770189176867532
267 
2.0031958922501047
94 
2.6662843795317808
 
51

Length

Max length19
Median length19
Mean length18.653782
Min length18

Characters and Unicode

Total characters22198
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0031958922501047
2nd row0.6770189176867532
3rd row-0.6491580568765987
4th row-0.6491580568765987
5th row-0.6491580568765987

Common Values

ValueCountFrequency (%)
-0.6491580568765987 778
65.4%
0.6770189176867532 267
 
22.4%
2.0031958922501047 94
 
7.9%
2.6662843795317808 51
 
4.3%

Length

2024-05-30T17:23:02.332932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:02.460560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6491580568765987 778
65.4%
0.6770189176867532 267
 
22.4%
2.0031958922501047 94
 
7.9%
2.6662843795317808 51
 
4.3%

Most occurring characters

ValueCountFrequency (%)
6 3288
14.8%
8 3115
14.0%
5 2840
12.8%
7 2820
12.7%
0 2517
11.3%
9 2062
9.3%
1 1551
7.0%
. 1190
 
5.4%
4 923
 
4.2%
- 778
 
3.5%
Other values (2) 1114
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 3288
14.8%
8 3115
14.0%
5 2840
12.8%
7 2820
12.7%
0 2517
11.3%
9 2062
9.3%
1 1551
7.0%
. 1190
 
5.4%
4 923
 
4.2%
- 778
 
3.5%
Other values (2) 1114
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 3288
14.8%
8 3115
14.0%
5 2840
12.8%
7 2820
12.7%
0 2517
11.3%
9 2062
9.3%
1 1551
7.0%
. 1190
 
5.4%
4 923
 
4.2%
- 778
 
3.5%
Other values (2) 1114
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 3288
14.8%
8 3115
14.0%
5 2840
12.8%
7 2820
12.7%
0 2517
11.3%
9 2062
9.3%
1 1551
7.0%
. 1190
 
5.4%
4 923
 
4.2%
- 778
 
3.5%
Other values (2) 1114
 
5.0%

STENOK_AN
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.9709474 × 10-18
Minimum-0.83648503
Maximum1.7147764
Zeros0
Zeros (%)0.0%
Negative688
Negative (%)57.8%
Memory size9.4 KiB
2024-05-30T17:23:02.585092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.83648503
5-th percentile-0.83648503
Q1-0.83648503
median-0.41127478
Q30.86435595
95-th percentile1.7147764
Maximum1.7147764
Range2.5512615
Interquartile range (IQR)1.700841

Descriptive statistics

Standard deviation1.0004204
Coefficient of variation (CV)-1.6754802 × 1017
Kurtosis-1.1054214
Mean-5.9709474 × 10-18
Median Absolute Deviation (MAD)0.42521024
Skewness0.73445785
Sum1.3322676 × 10-15
Variance1.000841
MonotonicityNot monotonic
2024-05-30T17:23:02.712107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.8364850254 569
47.8%
1.714776436 199
 
16.7%
-0.4112747818 119
 
10.0%
0.01393546176 93
 
7.8%
1.289566193 79
 
6.6%
0.4391457054 77
 
6.5%
0.864355949 54
 
4.5%
ValueCountFrequency (%)
-0.8364850254 569
47.8%
-0.4112747818 119
 
10.0%
0.01393546176 93
 
7.8%
0.4391457054 77
 
6.5%
0.864355949 54
 
4.5%
1.289566193 79
 
6.6%
1.714776436 199
 
16.7%
ValueCountFrequency (%)
1.714776436 199
 
16.7%
1.289566193 79
 
6.6%
0.864355949 54
 
4.5%
0.4391457054 77
 
6.5%
0.01393546176 93
 
7.8%
-0.4112747818 119
 
10.0%
-0.8364850254 569
47.8%

FK_STENOK
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
0.8320422000007952
586 
-1.0775628491813578
524 
-0.12276032459028124
 
40
1.7868447245918717
 
29
2.741647249182948
 
11

Length

Max length20
Median length18
Mean length18.498319
Min length17

Characters and Unicode

Total characters22013
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.12276032459028124
2nd row-1.0775628491813578
3rd row-1.0775628491813578
4th row-1.0775628491813578
5th row-1.0775628491813578

Common Values

ValueCountFrequency (%)
0.8320422000007952 586
49.2%
-1.0775628491813578 524
44.0%
-0.12276032459028124 40
 
3.4%
1.7868447245918717 29
 
2.4%
2.741647249182948 11
 
0.9%

Length

2024-05-30T17:23:02.871540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:03.024644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.8320422000007952 586
49.2%
1.0775628491813578 524
44.0%
0.12276032459028124 40
 
3.4%
1.7868447245918717 29
 
2.4%
2.741647249182948 11
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 4746
21.6%
2 3130
14.2%
7 2336
10.6%
8 2307
10.5%
1 1761
 
8.0%
5 1703
 
7.7%
4 1321
 
6.0%
9 1201
 
5.5%
. 1190
 
5.4%
3 1150
 
5.2%
Other values (2) 1168
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4746
21.6%
2 3130
14.2%
7 2336
10.6%
8 2307
10.5%
1 1761
 
8.0%
5 1703
 
7.7%
4 1321
 
6.0%
9 1201
 
5.5%
. 1190
 
5.4%
3 1150
 
5.2%
Other values (2) 1168
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4746
21.6%
2 3130
14.2%
7 2336
10.6%
8 2307
10.5%
1 1761
 
8.0%
5 1703
 
7.7%
4 1321
 
6.0%
9 1201
 
5.5%
. 1190
 
5.4%
3 1150
 
5.2%
Other values (2) 1168
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4746
21.6%
2 3130
14.2%
7 2336
10.6%
8 2307
10.5%
1 1761
 
8.0%
5 1703
 
7.7%
4 1321
 
6.0%
9 1201
 
5.5%
. 1190
 
5.4%
3 1150
 
5.2%
Other values (2) 1168
 
5.3%

IBS_POST
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size88.0 KiB
1.0807986209830092
474 
-0.1511461673022216
388 
-1.3830909555874524
328 

Length

Max length19
Median length19
Mean length18.601681
Min length18

Characters and Unicode

Total characters22136
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0807986209830092
2nd row-1.3830909555874524
3rd row1.0807986209830092
4th row1.0807986209830092
5th row1.0807986209830092

Common Values

ValueCountFrequency (%)
1.0807986209830092 474
39.8%
-0.1511461673022216 388
32.6%
-1.3830909555874524 328
27.6%

Length

2024-05-30T17:23:03.188679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:03.323685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0807986209830092 474
39.8%
0.1511461673022216 388
32.6%
1.3830909555874524 328
27.6%

Most occurring characters

ValueCountFrequency (%)
0 3802
17.2%
1 2742
12.4%
2 2440
11.0%
8 2078
9.4%
9 2078
9.4%
5 1700
7.7%
6 1638
7.4%
3 1518
 
6.9%
. 1190
 
5.4%
7 1190
 
5.4%
Other values (2) 1760
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3802
17.2%
1 2742
12.4%
2 2440
11.0%
8 2078
9.4%
9 2078
9.4%
5 1700
7.7%
6 1638
7.4%
3 1518
 
6.9%
. 1190
 
5.4%
7 1190
 
5.4%
Other values (2) 1760
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3802
17.2%
1 2742
12.4%
2 2440
11.0%
8 2078
9.4%
9 2078
9.4%
5 1700
7.7%
6 1638
7.4%
3 1518
 
6.9%
. 1190
 
5.4%
7 1190
 
5.4%
Other values (2) 1760
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3802
17.2%
1 2742
12.4%
2 2440
11.0%
8 2078
9.4%
9 2078
9.4%
5 1700
7.7%
6 1638
7.4%
3 1518
 
6.9%
. 1190
 
5.4%
7 1190
 
5.4%
Other values (2) 1760
8.0%

GB
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size87.7 KiB
0.5949813244904116
626 
-1.2560716850353137
439 
1.5205078292532743
119 
-0.330545180272451
 
6

Length

Max length19
Median length18
Mean length18.368908
Min length18

Characters and Unicode

Total characters21859
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.5205078292532743
2nd row-1.2560716850353137
3rd row0.5949813244904116
4th row0.5949813244904116
5th row1.5205078292532743

Common Values

ValueCountFrequency (%)
0.5949813244904116 626
52.6%
-1.2560716850353137 439
36.9%
1.5205078292532743 119
 
10.0%
-0.330545180272451 6
 
0.5%

Length

2024-05-30T17:23:03.696685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:03.830517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.5949813244904116 626
52.6%
1.2560716850353137 439
36.9%
1.5205078292532743 119
 
10.0%
0.330545180272451 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 3326
15.2%
4 2635
12.1%
0 2386
10.9%
5 2318
10.6%
3 2193
10.0%
9 1997
9.1%
2 1553
7.1%
6 1504
6.9%
. 1190
 
5.4%
8 1190
 
5.4%
Other values (2) 1567
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21859
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3326
15.2%
4 2635
12.1%
0 2386
10.9%
5 2318
10.6%
3 2193
10.0%
9 1997
9.1%
2 1553
7.1%
6 1504
6.9%
. 1190
 
5.4%
8 1190
 
5.4%
Other values (2) 1567
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21859
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3326
15.2%
4 2635
12.1%
0 2386
10.9%
5 2318
10.6%
3 2193
10.0%
9 1997
9.1%
2 1553
7.1%
6 1504
6.9%
. 1190
 
5.4%
8 1190
 
5.4%
Other values (2) 1567
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21859
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3326
15.2%
4 2635
12.1%
0 2386
10.9%
5 2318
10.6%
3 2193
10.0%
9 1997
9.1%
2 1553
7.1%
6 1504
6.9%
. 1190
 
5.4%
8 1190
 
5.4%
Other values (2) 1567
7.2%

SIM_GIPERT
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.1840749290264439
1151 
5.432570341267613
 
39

Length

Max length19
Median length19
Mean length18.934454
Min length17

Characters and Unicode

Total characters22532
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1840749290264439
2nd row-0.1840749290264439
3rd row-0.1840749290264439
4th row-0.1840749290264439
5th row-0.1840749290264439

Common Values

ValueCountFrequency (%)
-0.1840749290264439 1151
96.7%
5.432570341267613 39
 
3.3%

Length

2024-05-30T17:23:03.997394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:04.144477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1840749290264439 1151
96.7%
5.432570341267613 39
 
3.3%

Most occurring characters

ValueCountFrequency (%)
4 4682
20.8%
0 3492
15.5%
9 3453
15.3%
2 2380
10.6%
3 1268
 
5.6%
1 1229
 
5.5%
7 1229
 
5.5%
6 1229
 
5.5%
. 1190
 
5.3%
- 1151
 
5.1%
Other values (2) 1229
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4682
20.8%
0 3492
15.5%
9 3453
15.3%
2 2380
10.6%
3 1268
 
5.6%
1 1229
 
5.5%
7 1229
 
5.5%
6 1229
 
5.5%
. 1190
 
5.3%
- 1151
 
5.1%
Other values (2) 1229
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4682
20.8%
0 3492
15.5%
9 3453
15.3%
2 2380
10.6%
3 1268
 
5.6%
1 1229
 
5.5%
7 1229
 
5.5%
6 1229
 
5.5%
. 1190
 
5.3%
- 1151
 
5.1%
Other values (2) 1229
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4682
20.8%
0 3492
15.5%
9 3453
15.3%
2 2380
10.6%
3 1268
 
5.6%
1 1229
 
5.5%
7 1229
 
5.5%
6 1229
 
5.5%
. 1190
 
5.3%
- 1151
 
5.1%
Other values (2) 1229
 
5.5%

ZSN_A
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:04.278634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:04.405053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

nr_11
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
-0.17149858514250885
1156 
5.830951894845301
 
34

Length

Max length20
Median length20
Mean length19.914286
Min length17

Characters and Unicode

Total characters23698
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.17149858514250885
2nd row-0.17149858514250885
3rd row-0.17149858514250885
4th row-0.17149858514250885
5th row-0.17149858514250885

Common Values

ValueCountFrequency (%)
-0.17149858514250885 1156
97.1%
5.830951894845301 34
 
2.9%

Length

2024-05-30T17:23:04.542845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:04.669339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.17149858514250885 1156
97.1%
5.830951894845301 34
 
2.9%

Most occurring characters

ValueCountFrequency (%)
8 4726
19.9%
5 4726
19.9%
1 3536
14.9%
0 2380
10.0%
4 2380
10.0%
9 1224
 
5.2%
. 1190
 
5.0%
- 1156
 
4.9%
7 1156
 
4.9%
2 1156
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 4726
19.9%
5 4726
19.9%
1 3536
14.9%
0 2380
10.0%
4 2380
10.0%
9 1224
 
5.2%
. 1190
 
5.0%
- 1156
 
4.9%
7 1156
 
4.9%
2 1156
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 4726
19.9%
5 4726
19.9%
1 3536
14.9%
0 2380
10.0%
4 2380
10.0%
9 1224
 
5.2%
. 1190
 
5.0%
- 1156
 
4.9%
7 1156
 
4.9%
2 1156
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 4726
19.9%
5 4726
19.9%
1 3536
14.9%
0 2380
10.0%
4 2380
10.0%
9 1224
 
5.2%
. 1190
 
5.0%
- 1156
 
4.9%
7 1156
 
4.9%
2 1156
 
4.9%

nr_01
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.04103049699311091
1188 
24.37211521390788
 
2

Length

Max length20
Median length20
Mean length19.994958
Min length17

Characters and Unicode

Total characters23794
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04103049699311091
2nd row-0.04103049699311091
3rd row-0.04103049699311091
4th row-0.04103049699311091
5th row-0.04103049699311091

Common Values

ValueCountFrequency (%)
-0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Length

2024-05-30T17:23:04.806431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:04.935780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

nr_02
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.11298653657320641
1175 
8.850612031567834
 
15

Length

Max length20
Median length20
Mean length19.962185
Min length17

Characters and Unicode

Total characters23755
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.11298653657320641
2nd row-0.11298653657320641
3rd row-0.11298653657320641
4th row-0.11298653657320641
5th row-0.11298653657320641

Common Values

ValueCountFrequency (%)
-0.11298653657320641 1175
98.7%
8.850612031567834 15
 
1.3%

Length

2024-05-30T17:23:05.074965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:05.203269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.11298653657320641 1175
98.7%
8.850612031567834 15
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 3555
15.0%
6 3555
15.0%
0 2380
10.0%
5 2380
10.0%
3 2380
10.0%
2 2365
10.0%
8 1220
 
5.1%
. 1190
 
5.0%
7 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2350
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3555
15.0%
6 3555
15.0%
0 2380
10.0%
5 2380
10.0%
3 2380
10.0%
2 2365
10.0%
8 1220
 
5.1%
. 1190
 
5.0%
7 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2350
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3555
15.0%
6 3555
15.0%
0 2380
10.0%
5 2380
10.0%
3 2380
10.0%
2 2365
10.0%
8 1220
 
5.1%
. 1190
 
5.0%
7 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2350
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3555
15.0%
6 3555
15.0%
0 2380
10.0%
5 2380
10.0%
3 2380
10.0%
2 2365
10.0%
8 1220
 
5.1%
. 1190
 
5.0%
7 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2350
9.9%

nr_03
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.1340301154826311
1169 
7.461009761866464
 
21

Length

Max length19
Median length19
Mean length18.964706
Min length17

Characters and Unicode

Total characters22568
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1340301154826311
2nd row-0.1340301154826311
3rd row-0.1340301154826311
4th row-0.1340301154826311
5th row-0.1340301154826311

Common Values

ValueCountFrequency (%)
-0.1340301154826311 1169
98.2%
7.461009761866464 21
 
1.8%

Length

2024-05-30T17:23:05.350490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:05.489798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1340301154826311 1169
98.2%
7.461009761866464 21
 
1.8%

Most occurring characters

ValueCountFrequency (%)
1 5887
26.1%
0 3549
15.7%
3 3507
15.5%
4 2401
10.6%
6 1274
 
5.6%
. 1190
 
5.3%
8 1190
 
5.3%
- 1169
 
5.2%
5 1169
 
5.2%
2 1169
 
5.2%
Other values (2) 63
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5887
26.1%
0 3549
15.7%
3 3507
15.5%
4 2401
10.6%
6 1274
 
5.6%
. 1190
 
5.3%
8 1190
 
5.3%
- 1169
 
5.2%
5 1169
 
5.2%
2 1169
 
5.2%
Other values (2) 63
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5887
26.1%
0 3549
15.7%
3 3507
15.5%
4 2401
10.6%
6 1274
 
5.6%
. 1190
 
5.3%
8 1190
 
5.3%
- 1169
 
5.2%
5 1169
 
5.2%
2 1169
 
5.2%
Other values (2) 63
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5887
26.1%
0 3549
15.7%
3 3507
15.5%
4 2401
10.6%
6 1274
 
5.6%
. 1190
 
5.3%
8 1190
 
5.3%
- 1169
 
5.2%
5 1169
 
5.2%
2 1169
 
5.2%
Other values (2) 63
 
0.3%

nr_04
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.1091089451179962
1176 
9.165151389911681
 
14

Length

Max length19
Median length19
Mean length18.976471
Min length17

Characters and Unicode

Total characters22582
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1091089451179962
2nd row-0.1091089451179962
3rd row-0.1091089451179962
4th row-0.1091089451179962
5th row-0.1091089451179962

Common Values

ValueCountFrequency (%)
-0.1091089451179962 1176
98.8%
9.165151389911681 14
 
1.2%

Length

2024-05-30T17:23:05.637791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:05.777791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1091089451179962 1176
98.8%
9.165151389911681 14
 
1.2%

Most occurring characters

ValueCountFrequency (%)
1 4788
21.2%
9 4746
21.0%
0 3528
15.6%
8 1204
 
5.3%
5 1204
 
5.3%
6 1204
 
5.3%
. 1190
 
5.3%
- 1176
 
5.2%
4 1176
 
5.2%
7 1176
 
5.2%
Other values (2) 1190
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4788
21.2%
9 4746
21.0%
0 3528
15.6%
8 1204
 
5.3%
5 1204
 
5.3%
6 1204
 
5.3%
. 1190
 
5.3%
- 1176
 
5.2%
4 1176
 
5.2%
7 1176
 
5.2%
Other values (2) 1190
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4788
21.2%
9 4746
21.0%
0 3528
15.6%
8 1204
 
5.3%
5 1204
 
5.3%
6 1204
 
5.3%
. 1190
 
5.3%
- 1176
 
5.2%
4 1176
 
5.2%
7 1176
 
5.2%
Other values (2) 1190
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4788
21.2%
9 4746
21.0%
0 3528
15.6%
8 1204
 
5.3%
5 1204
 
5.3%
6 1204
 
5.3%
. 1190
 
5.3%
- 1176
 
5.2%
4 1176
 
5.2%
7 1176
 
5.2%
Other values (2) 1190
 
5.3%

nr_07
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.02900073952828708
1189 
34.48187929913333
 
1

Length

Max length20
Median length20
Mean length19.997479
Min length17

Characters and Unicode

Total characters23797
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row-0.02900073952828708
2nd row-0.02900073952828708
3rd row-0.02900073952828708
4th row-0.02900073952828708
5th row-0.02900073952828708

Common Values

ValueCountFrequency (%)
-0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Length

2024-05-30T17:23:05.920688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:06.046338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

nr_08
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.04103049699311091
1188 
24.37211521390788
 
2

Length

Max length20
Median length20
Mean length19.994958
Min length17

Characters and Unicode

Total characters23794
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04103049699311091
2nd row-0.04103049699311091
3rd row-0.04103049699311091
4th row-0.04103049699311091
5th row-0.04103049699311091

Common Values

ValueCountFrequency (%)
-0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Length

2024-05-30T17:23:06.192499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:06.322721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

np_01
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.02900073952828708
1189 
34.48187929913333
 
1

Length

Max length20
Median length20
Mean length19.997479
Min length17

Characters and Unicode

Total characters23797
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row-0.02900073952828708
2nd row-0.02900073952828708
3rd row-0.02900073952828708
4th row-0.02900073952828708
5th row-0.02900073952828708

Common Values

ValueCountFrequency (%)
-0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Length

2024-05-30T17:23:06.465550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:06.590227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

np_04
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.04103049699311091
1188 
24.37211521390788
 
2

Length

Max length20
Median length20
Mean length19.994958
Min length17

Characters and Unicode

Total characters23794
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04103049699311091
2nd row-0.04103049699311091
3rd row-0.04103049699311091
4th row-0.04103049699311091
5th row-0.04103049699311091

Common Values

ValueCountFrequency (%)
-0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Length

2024-05-30T17:23:06.727752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:06.854902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

np_05
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.09205746178983233
1180 
10.862780491200215
 
10

Length

Max length20
Median length20
Mean length19.983193
Min length18

Characters and Unicode

Total characters23780
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.09205746178983233
2nd row-0.09205746178983233
3rd row-0.09205746178983233
4th row-0.09205746178983233
5th row-0.09205746178983233

Common Values

ValueCountFrequency (%)
-0.09205746178983233 1180
99.2%
10.862780491200215 10
 
0.8%

Length

2024-05-30T17:23:07.000100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:07.139784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09205746178983233 1180
99.2%
10.862780491200215 10
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 3580
15.1%
3 3540
14.9%
2 2390
10.1%
8 2380
10.0%
9 2370
10.0%
7 2370
10.0%
1 1210
 
5.1%
. 1190
 
5.0%
5 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2370
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3580
15.1%
3 3540
14.9%
2 2390
10.1%
8 2380
10.0%
9 2370
10.0%
7 2370
10.0%
1 1210
 
5.1%
. 1190
 
5.0%
5 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2370
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3580
15.1%
3 3540
14.9%
2 2390
10.1%
8 2380
10.0%
9 2370
10.0%
7 2370
10.0%
1 1210
 
5.1%
. 1190
 
5.0%
5 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2370
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3580
15.1%
3 3540
14.9%
2 2390
10.1%
8 2380
10.0%
9 2370
10.0%
7 2370
10.0%
1 1210
 
5.1%
. 1190
 
5.0%
5 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2370
10.0%

np_07
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size90.8 KiB
-0.029000739528287082
1189 
34.48187929913334
 
1

Length

Max length21
Median length21
Mean length20.996639
Min length17

Characters and Unicode

Total characters24986
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row-0.029000739528287082
2nd row-0.029000739528287082
3rd row-0.029000739528287082
4th row-0.029000739528287082
5th row-0.029000739528287082

Common Values

ValueCountFrequency (%)
-0.029000739528287082 1189
99.9%
34.48187929913334 1
 
0.1%

Length

2024-05-30T17:23:07.284647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:07.426242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.029000739528287082 1189
99.9%
34.48187929913334 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7134
28.6%
2 4757
19.0%
8 3569
14.3%
9 2381
 
9.5%
7 2379
 
9.5%
3 1193
 
4.8%
. 1190
 
4.8%
- 1189
 
4.8%
5 1189
 
4.8%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7134
28.6%
2 4757
19.0%
8 3569
14.3%
9 2381
 
9.5%
7 2379
 
9.5%
3 1193
 
4.8%
. 1190
 
4.8%
- 1189
 
4.8%
5 1189
 
4.8%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7134
28.6%
2 4757
19.0%
8 3569
14.3%
9 2381
 
9.5%
7 2379
 
9.5%
3 1193
 
4.8%
. 1190
 
4.8%
- 1189
 
4.8%
5 1189
 
4.8%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7134
28.6%
2 4757
19.0%
8 3569
14.3%
9 2381
 
9.5%
7 2379
 
9.5%
3 1193
 
4.8%
. 1190
 
4.8%
- 1189
 
4.8%
5 1189
 
4.8%
4 3
 
< 0.1%

np_08
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size90.8 KiB
-0.050273053910145554
1187 
19.891371663780923
 
3

Length

Max length21
Median length21
Mean length20.992437
Min length18

Characters and Unicode

Total characters24981
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.050273053910145554
2nd row-0.050273053910145554
3rd row-0.050273053910145554
4th row-0.050273053910145554
5th row-0.050273053910145554

Common Values

ValueCountFrequency (%)
-0.050273053910145554 1187
99.7%
19.891371663780923 3
 
0.3%

Length

2024-05-30T17:23:07.568758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:07.695757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.050273053910145554 1187
99.7%
19.891371663780923 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

np_09
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.04103049699311091
1188 
24.37211521390788
 
2

Length

Max length20
Median length20
Mean length19.994958
Min length17

Characters and Unicode

Total characters23794
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04103049699311091
2nd row-0.04103049699311091
3rd row-0.04103049699311091
4th row-0.04103049699311091
5th row-0.04103049699311091

Common Values

ValueCountFrequency (%)
-0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Length

2024-05-30T17:23:07.838758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:07.967758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

np_10
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.02900073952828708
1189 
34.48187929913333
 
1

Length

Max length20
Median length20
Mean length19.997479
Min length17

Characters and Unicode

Total characters23797
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row-0.02900073952828708
2nd row-0.02900073952828708
3rd row-0.02900073952828708
4th row-0.02900073952828708
5th row-0.02900073952828708

Common Values

ValueCountFrequency (%)
-0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Length

2024-05-30T17:23:08.119987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:08.245025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

endocr_01
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.3 KiB
-0.3606999631796443
1053 
2.7723873082347845
137 

Length

Max length19
Median length19
Mean length18.884874
Min length18

Characters and Unicode

Total characters22473
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.3606999631796443
2nd row-0.3606999631796443
3rd row-0.3606999631796443
4th row-0.3606999631796443
5th row-0.3606999631796443

Common Values

ValueCountFrequency (%)
-0.3606999631796443 1053
88.5%
2.7723873082347845 137
 
11.5%

Length

2024-05-30T17:23:08.376756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:08.499136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.3606999631796443 1053
88.5%
2.7723873082347845 137
 
11.5%

Most occurring characters

ValueCountFrequency (%)
6 4212
18.7%
9 4212
18.7%
3 3570
15.9%
4 2380
10.6%
0 2243
10.0%
7 1601
 
7.1%
. 1190
 
5.3%
- 1053
 
4.7%
1 1053
 
4.7%
2 411
 
1.8%
Other values (2) 548
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22473
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 4212
18.7%
9 4212
18.7%
3 3570
15.9%
4 2380
10.6%
0 2243
10.0%
7 1601
 
7.1%
. 1190
 
5.3%
- 1053
 
4.7%
1 1053
 
4.7%
2 411
 
1.8%
Other values (2) 548
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22473
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 4212
18.7%
9 4212
18.7%
3 3570
15.9%
4 2380
10.6%
0 2243
10.0%
7 1601
 
7.1%
. 1190
 
5.3%
- 1053
 
4.7%
1 1053
 
4.7%
2 411
 
1.8%
Other values (2) 548
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22473
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 4212
18.7%
9 4212
18.7%
3 3570
15.9%
4 2380
10.6%
0 2243
10.0%
7 1601
 
7.1%
. 1190
 
5.3%
- 1053
 
4.7%
1 1053
 
4.7%
2 411
 
1.8%
Other values (2) 548
 
2.4%

endocr_02
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
-0.13724291033913613
1168 
7.286350876186864
 
22

Length

Max length20
Median length20
Mean length19.944538
Min length17

Characters and Unicode

Total characters23734
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.13724291033913613
2nd row-0.13724291033913613
3rd row-0.13724291033913613
4th row-0.13724291033913613
5th row-0.13724291033913613

Common Values

ValueCountFrequency (%)
-0.13724291033913613 1168
98.2%
7.286350876186864 22
 
1.8%

Length

2024-05-30T17:23:08.639484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:08.767834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.13724291033913613 1168
98.2%
7.286350876186864 22
 
1.8%

Most occurring characters

ValueCountFrequency (%)
3 5862
24.7%
1 4694
19.8%
0 2358
9.9%
2 2358
9.9%
9 2336
 
9.8%
6 1256
 
5.3%
7 1212
 
5.1%
. 1190
 
5.0%
4 1190
 
5.0%
- 1168
 
4.9%
Other values (2) 110
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 5862
24.7%
1 4694
19.8%
0 2358
9.9%
2 2358
9.9%
9 2336
 
9.8%
6 1256
 
5.3%
7 1212
 
5.1%
. 1190
 
5.0%
4 1190
 
5.0%
- 1168
 
4.9%
Other values (2) 110
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 5862
24.7%
1 4694
19.8%
0 2358
9.9%
2 2358
9.9%
9 2336
 
9.8%
6 1256
 
5.3%
7 1212
 
5.1%
. 1190
 
5.0%
4 1190
 
5.0%
- 1168
 
4.9%
Other values (2) 110
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 5862
24.7%
1 4694
19.8%
0 2358
9.9%
2 2358
9.9%
9 2336
 
9.8%
6 1256
 
5.3%
7 1212
 
5.1%
. 1190
 
5.0%
4 1190
 
5.0%
- 1168
 
4.9%
Other values (2) 110
 
0.5%

endocr_03
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:08.899281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:09.010884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

zab_leg_01
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.3 KiB
-0.3379825583337515
1068 
2.9587325598397265
122 

Length

Max length19
Median length19
Mean length18.897479
Min length18

Characters and Unicode

Total characters22488
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.3379825583337515
2nd row-0.3379825583337515
3rd row-0.3379825583337515
4th row2.9587325598397265
5th row-0.3379825583337515

Common Values

ValueCountFrequency (%)
-0.3379825583337515 1068
89.7%
2.9587325598397265 122
 
10.3%

Length

2024-05-30T17:23:09.135962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:09.263102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.3379825583337515 1068
89.7%
2.9587325598397265 122
 
10.3%

Most occurring characters

ValueCountFrequency (%)
3 5584
24.8%
5 4760
21.2%
7 2380
10.6%
8 2380
10.6%
9 1434
 
6.4%
2 1434
 
6.4%
. 1190
 
5.3%
- 1068
 
4.7%
0 1068
 
4.7%
1 1068
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 5584
24.8%
5 4760
21.2%
7 2380
10.6%
8 2380
10.6%
9 1434
 
6.4%
2 1434
 
6.4%
. 1190
 
5.3%
- 1068
 
4.7%
0 1068
 
4.7%
1 1068
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 5584
24.8%
5 4760
21.2%
7 2380
10.6%
8 2380
10.6%
9 1434
 
6.4%
2 1434
 
6.4%
. 1190
 
5.3%
- 1068
 
4.7%
0 1068
 
4.7%
1 1068
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 5584
24.8%
5 4760
21.2%
7 2380
10.6%
8 2380
10.6%
9 1434
 
6.4%
2 1434
 
6.4%
. 1190
 
5.3%
- 1068
 
4.7%
0 1068
 
4.7%
1 1068
 
4.7%

zab_leg_02
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
-0.22635536025596517
1132 
4.417832203616424
 
58

Length

Max length20
Median length20
Mean length19.853782
Min length17

Characters and Unicode

Total characters23626
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.22635536025596517
2nd row-0.22635536025596517
3rd row-0.22635536025596517
4th row-0.22635536025596517
5th row-0.22635536025596517

Common Values

ValueCountFrequency (%)
-0.22635536025596517 1132
95.1%
4.417832203616424 58
 
4.9%

Length

2024-05-30T17:23:09.409103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:09.535475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.22635536025596517 1132
95.1%
4.417832203616424 58
 
4.9%

Most occurring characters

ValueCountFrequency (%)
5 5660
24.0%
2 3570
15.1%
6 3512
14.9%
3 2380
10.1%
0 2322
9.8%
1 1248
 
5.3%
. 1190
 
5.0%
7 1190
 
5.0%
- 1132
 
4.8%
9 1132
 
4.8%
Other values (2) 290
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 5660
24.0%
2 3570
15.1%
6 3512
14.9%
3 2380
10.1%
0 2322
9.8%
1 1248
 
5.3%
. 1190
 
5.0%
7 1190
 
5.0%
- 1132
 
4.8%
9 1132
 
4.8%
Other values (2) 290
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 5660
24.0%
2 3570
15.1%
6 3512
14.9%
3 2380
10.1%
0 2322
9.8%
1 1248
 
5.3%
. 1190
 
5.0%
7 1190
 
5.0%
- 1132
 
4.8%
9 1132
 
4.8%
Other values (2) 290
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 5660
24.0%
2 3570
15.1%
6 3512
14.9%
3 2380
10.1%
0 2322
9.8%
1 1248
 
5.3%
. 1190
 
5.0%
7 1190
 
5.0%
- 1132
 
4.8%
9 1132
 
4.8%
Other values (2) 290
 
1.2%

zab_leg_03
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.10910894511799618
1176 
9.16515138991168
 
14

Length

Max length20
Median length20
Mean length19.952941
Min length16

Characters and Unicode

Total characters23744
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.10910894511799618
2nd row-0.10910894511799618
3rd row-0.10910894511799618
4th row-0.10910894511799618
5th row-0.10910894511799618

Common Values

ValueCountFrequency (%)
-0.10910894511799618 1176
98.8%
9.16515138991168 14
 
1.2%

Length

2024-05-30T17:23:09.683597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:09.820353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.10910894511799618 1176
98.8%
9.16515138991168 14
 
1.2%

Most occurring characters

ValueCountFrequency (%)
1 5950
25.1%
9 4746
20.0%
0 3528
14.9%
8 2380
 
10.0%
5 1204
 
5.1%
6 1204
 
5.1%
. 1190
 
5.0%
- 1176
 
5.0%
4 1176
 
5.0%
7 1176
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5950
25.1%
9 4746
20.0%
0 3528
14.9%
8 2380
 
10.0%
5 1204
 
5.1%
6 1204
 
5.1%
. 1190
 
5.0%
- 1176
 
5.0%
4 1176
 
5.0%
7 1176
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5950
25.1%
9 4746
20.0%
0 3528
14.9%
8 2380
 
10.0%
5 1204
 
5.1%
6 1204
 
5.1%
. 1190
 
5.0%
- 1176
 
5.0%
4 1176
 
5.0%
7 1176
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5950
25.1%
9 4746
20.0%
0 3528
14.9%
8 2380
 
10.0%
5 1204
 
5.1%
6 1204
 
5.1%
. 1190
 
5.0%
- 1176
 
5.0%
4 1176
 
5.0%
7 1176
 
5.0%

zab_leg_04
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size90.8 KiB
-0.050273053910145554
1187 
19.891371663780923
 
3

Length

Max length21
Median length21
Mean length20.992437
Min length18

Characters and Unicode

Total characters24981
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.050273053910145554
2nd row-0.050273053910145554
3rd row-0.050273053910145554
4th row-0.050273053910145554
5th row-0.050273053910145554

Common Values

ValueCountFrequency (%)
-0.050273053910145554 1187
99.7%
19.891371663780923 3
 
0.3%

Length

2024-05-30T17:23:09.965462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:10.096395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.050273053910145554 1187
99.7%
19.891371663780923 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

zab_leg_06
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.1273791518842727
1171 
7.850578255604387
 
19

Length

Max length19
Median length19
Mean length18.968067
Min length17

Characters and Unicode

Total characters22572
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1273791518842727
2nd row-0.1273791518842727
3rd row-0.1273791518842727
4th row-0.1273791518842727
5th row-0.1273791518842727

Common Values

ValueCountFrequency (%)
-0.1273791518842727 1171
98.4%
7.850578255604387 19
 
1.6%

Length

2024-05-30T17:23:10.250312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:10.390307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1273791518842727 1171
98.4%
7.850578255604387 19
 
1.6%

Most occurring characters

ValueCountFrequency (%)
7 4741
21.0%
2 3532
15.6%
1 3513
15.6%
8 2399
10.6%
5 1247
 
5.5%
0 1209
 
5.4%
. 1190
 
5.3%
3 1190
 
5.3%
4 1190
 
5.3%
- 1171
 
5.2%
Other values (2) 1190
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22572
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 4741
21.0%
2 3532
15.6%
1 3513
15.6%
8 2399
10.6%
5 1247
 
5.5%
0 1209
 
5.4%
. 1190
 
5.3%
3 1190
 
5.3%
4 1190
 
5.3%
- 1171
 
5.2%
Other values (2) 1190
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22572
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 4741
21.0%
2 3532
15.6%
1 3513
15.6%
8 2399
10.6%
5 1247
 
5.5%
0 1209
 
5.4%
. 1190
 
5.3%
3 1190
 
5.3%
4 1190
 
5.3%
- 1171
 
5.2%
Other values (2) 1190
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22572
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 4741
21.0%
2 3532
15.6%
1 3513
15.6%
8 2399
10.6%
5 1247
 
5.5%
0 1209
 
5.4%
. 1190
 
5.3%
3 1190
 
5.3%
4 1190
 
5.3%
- 1171
 
5.2%
Other values (2) 1190
 
5.3%

O_L_POST
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.3 KiB
-0.2137620180282125
1138 
4.678099548386651
 
52

Length

Max length19
Median length19
Mean length18.912605
Min length17

Characters and Unicode

Total characters22506
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.2137620180282125
2nd row-0.2137620180282125
3rd row-0.2137620180282125
4th row-0.2137620180282125
5th row-0.2137620180282125

Common Values

ValueCountFrequency (%)
-0.2137620180282125 1138
95.6%
4.678099548386651 52
 
4.4%

Length

2024-05-30T17:23:10.538527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:10.671209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2137620180282125 1138
95.6%
4.678099548386651 52
 
4.4%

Most occurring characters

ValueCountFrequency (%)
2 5690
25.3%
0 3466
15.4%
1 3466
15.4%
8 2432
10.8%
6 1294
 
5.7%
5 1242
 
5.5%
. 1190
 
5.3%
3 1190
 
5.3%
7 1190
 
5.3%
- 1138
 
5.1%
Other values (2) 208
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5690
25.3%
0 3466
15.4%
1 3466
15.4%
8 2432
10.8%
6 1294
 
5.7%
5 1242
 
5.5%
. 1190
 
5.3%
3 1190
 
5.3%
7 1190
 
5.3%
- 1138
 
5.1%
Other values (2) 208
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5690
25.3%
0 3466
15.4%
1 3466
15.4%
8 2432
10.8%
6 1294
 
5.7%
5 1242
 
5.5%
. 1190
 
5.3%
3 1190
 
5.3%
7 1190
 
5.3%
- 1138
 
5.1%
Other values (2) 208
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5690
25.3%
0 3466
15.4%
1 3466
15.4%
8 2432
10.8%
6 1294
 
5.7%
5 1242
 
5.5%
. 1190
 
5.3%
3 1190
 
5.3%
7 1190
 
5.3%
- 1138
 
5.1%
Other values (2) 208
 
0.9%

K_SH_POST
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.04103049699311091
1188 
24.37211521390788
 
2

Length

Max length20
Median length20
Mean length19.994958
Min length17

Characters and Unicode

Total characters23794
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04103049699311091
2nd row-0.04103049699311091
3rd row-0.04103049699311091
4th row-0.04103049699311091
5th row-0.04103049699311091

Common Values

ValueCountFrequency (%)
-0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Length

2024-05-30T17:23:10.809736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:10.953683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

MP_TP_POST
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
-0.21590251554374687
1137 
4.631720003268683
 
53

Length

Max length20
Median length20
Mean length19.866387
Min length17

Characters and Unicode

Total characters23641
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.21590251554374687
2nd row-0.21590251554374687
3rd row-0.21590251554374687
4th row-0.21590251554374687
5th row-0.21590251554374687

Common Values

ValueCountFrequency (%)
-0.21590251554374687 1137
95.5%
4.631720003268683 53
 
4.5%

Length

2024-05-30T17:23:11.106321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:11.232270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.21590251554374687 1137
95.5%
4.631720003268683 53
 
4.5%

Most occurring characters

ValueCountFrequency (%)
5 4548
19.2%
0 2433
10.3%
2 2380
10.1%
1 2327
9.8%
4 2327
9.8%
7 2327
9.8%
3 1296
 
5.5%
6 1296
 
5.5%
8 1243
 
5.3%
. 1190
 
5.0%
Other values (2) 2274
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 4548
19.2%
0 2433
10.3%
2 2380
10.1%
1 2327
9.8%
4 2327
9.8%
7 2327
9.8%
3 1296
 
5.5%
6 1296
 
5.5%
8 1243
 
5.3%
. 1190
 
5.0%
Other values (2) 2274
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 4548
19.2%
0 2433
10.3%
2 2380
10.1%
1 2327
9.8%
4 2327
9.8%
7 2327
9.8%
3 1296
 
5.5%
6 1296
 
5.5%
8 1243
 
5.3%
. 1190
 
5.0%
Other values (2) 2274
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 4548
19.2%
0 2433
10.3%
2 2380
10.1%
1 2327
9.8%
4 2327
9.8%
7 2327
9.8%
3 1296
 
5.5%
6 1296
 
5.5%
8 1243
 
5.3%
. 1190
 
5.0%
Other values (2) 2274
9.6%

SVT_POST
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.07118684968143742
1184 
14.047538337136983
 
6

Length

Max length20
Median length20
Mean length19.989916
Min length18

Characters and Unicode

Total characters23788
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.07118684968143742
2nd row-0.07118684968143742
3rd row-0.07118684968143742
4th row-0.07118684968143742
5th row-0.07118684968143742

Common Values

ValueCountFrequency (%)
-0.07118684968143742 1184
99.5%
14.047538337136983 6
 
0.5%

Length

2024-05-30T17:23:11.378680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:11.517265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07118684968143742 1184
99.5%
14.047538337136983 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

GT_POST
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.06495698024616309
1185 
15.39480431834065
 
5

Length

Max length20
Median length20
Mean length19.987395
Min length17

Characters and Unicode

Total characters23785
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.06495698024616309
2nd row-0.06495698024616309
3rd row-0.06495698024616309
4th row-0.06495698024616309
5th row-0.06495698024616309

Common Values

ValueCountFrequency (%)
-0.06495698024616309 1185
99.6%
15.39480431834065 5
 
0.4%

Length

2024-05-30T17:23:11.654786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:11.783809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06495698024616309 1185
99.6%
15.39480431834065 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

FIB_G_POST
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
-0.07692307692307691
1183 
13.0
 
7

Length

Max length20
Median length20
Mean length19.905882
Min length4

Characters and Unicode

Total characters23688
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.07692307692307691
2nd row-0.07692307692307691
3rd row-0.07692307692307691
4th row-0.07692307692307691
5th row-0.07692307692307691

Common Values

ValueCountFrequency (%)
-0.07692307692307691 1183
99.4%
13.0 7
 
0.6%

Length

2024-05-30T17:23:11.932138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:12.071053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07692307692307691 1183
99.4%
13.0 7
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 4739
20.0%
7 3549
15.0%
6 3549
15.0%
9 3549
15.0%
3 2373
10.0%
2 2366
10.0%
. 1190
 
5.0%
1 1190
 
5.0%
- 1183
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4739
20.0%
7 3549
15.0%
6 3549
15.0%
9 3549
15.0%
3 2373
10.0%
2 2366
10.0%
. 1190
 
5.0%
1 1190
 
5.0%
- 1183
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4739
20.0%
7 3549
15.0%
6 3549
15.0%
9 3549
15.0%
3 2373
10.0%
2 2366
10.0%
. 1190
 
5.0%
1 1190
 
5.0%
- 1183
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4739
20.0%
7 3549
15.0%
6 3549
15.0%
9 3549
15.0%
3 2373
10.0%
2 2366
10.0%
. 1190
 
5.0%
1 1190
 
5.0%
- 1183
 
5.0%

ant_im
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.8766268782311168
494 
-0.26728927011486153
328 
1.5607235542339042
314 
0.3420483380013937
 
34
0.9513859461176489
 
20

Length

Max length20
Median length19
Mean length18.966387
Min length18

Characters and Unicode

Total characters22570
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.26728927011486153
2nd row1.5607235542339042
3rd row1.5607235542339042
4th row-0.8766268782311168
5th row1.5607235542339042

Common Values

ValueCountFrequency (%)
-0.8766268782311168 494
41.5%
-0.26728927011486153 328
27.6%
1.5607235542339042 314
26.4%
0.3420483380013937 34
 
2.9%
0.9513859461176489 20
 
1.7%

Length

2024-05-30T17:23:12.229666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:12.387637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.8766268782311168 494
41.5%
0.26728927011486153 328
27.6%
1.5607235542339042 314
26.4%
0.3420483380013937 34
 
2.9%
0.9513859461176489 20
 
1.7%

Most occurring characters

ValueCountFrequency (%)
6 2986
13.2%
2 2948
13.1%
1 2874
12.7%
8 2740
12.1%
7 2012
8.9%
3 1954
8.7%
0 1934
8.6%
5 1310
5.8%
. 1190
 
5.3%
4 1064
 
4.7%
Other values (2) 1558
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 2986
13.2%
2 2948
13.1%
1 2874
12.7%
8 2740
12.1%
7 2012
8.9%
3 1954
8.7%
0 1934
8.6%
5 1310
5.8%
. 1190
 
5.3%
4 1064
 
4.7%
Other values (2) 1558
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 2986
13.2%
2 2948
13.1%
1 2874
12.7%
8 2740
12.1%
7 2012
8.9%
3 1954
8.7%
0 1934
8.6%
5 1310
5.8%
. 1190
 
5.3%
4 1064
 
4.7%
Other values (2) 1558
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 2986
13.2%
2 2948
13.1%
1 2874
12.7%
8 2740
12.1%
7 2012
8.9%
3 1954
8.7%
0 1934
8.6%
5 1310
5.8%
. 1190
 
5.3%
4 1064
 
4.7%
Other values (2) 1558
6.9%

lat_im
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size87.7 KiB
0.3088287090680236
645 
-1.1267422432403673
418 
2.4621851375306103
70 
1.7443996613764148
 
57

Length

Max length19
Median length18
Mean length18.351261
Min length18

Characters and Unicode

Total characters21838
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.1267422432403673
2nd row0.3088287090680236
3rd row0.3088287090680236
4th row0.3088287090680236
5th row0.3088287090680236

Common Values

ValueCountFrequency (%)
0.3088287090680236 645
54.2%
-1.1267422432403673 418
35.1%
2.4621851375306103 70
 
5.9%
1.7443996613764148 57
 
4.8%

Length

2024-05-30T17:23:12.559108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:12.685771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.3088287090680236 645
54.2%
1.1267422432403673 418
35.1%
2.4621851375306103 70
 
5.9%
1.7443996613764148 57
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 3783
17.3%
2 3102
14.2%
3 2868
13.1%
8 2707
12.4%
6 2437
11.2%
7 1665
7.6%
4 1552
7.1%
1 1217
 
5.6%
. 1190
 
5.4%
9 759
 
3.5%
Other values (2) 558
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3783
17.3%
2 3102
14.2%
3 2868
13.1%
8 2707
12.4%
6 2437
11.2%
7 1665
7.6%
4 1552
7.1%
1 1217
 
5.6%
. 1190
 
5.4%
9 759
 
3.5%
Other values (2) 558
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3783
17.3%
2 3102
14.2%
3 2868
13.1%
8 2707
12.4%
6 2437
11.2%
7 1665
7.6%
4 1552
7.1%
1 1217
 
5.6%
. 1190
 
5.4%
9 759
 
3.5%
Other values (2) 558
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3783
17.3%
2 3102
14.2%
3 2868
13.1%
8 2707
12.4%
6 2437
11.2%
7 1665
7.6%
4 1552
7.1%
1 1217
 
5.6%
. 1190
 
5.4%
9 759
 
3.5%
Other values (2) 558
 
2.6%

inf_im
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size88.0 KiB
-0.6924953148533707
709 
0.05394438141067921
156 
0.8003840776747291
134 
2.2932634702028287
107 
1.546823773938779
84 

Length

Max length19
Median length19
Mean length18.656303
Min length17

Characters and Unicode

Total characters22201
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.6924953148533707
2nd row-0.6924953148533707
3rd row-0.6924953148533707
4th row0.05394438141067921
5th row-0.6924953148533707

Common Values

ValueCountFrequency (%)
-0.6924953148533707 709
59.6%
0.05394438141067921 156
 
13.1%
0.8003840776747291 134
 
11.3%
2.2932634702028287 107
 
9.0%
1.546823773938779 84
 
7.1%

Length

2024-05-30T17:23:12.848952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:13.039686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6924953148533707 709
59.6%
0.05394438141067921 156
 
13.1%
0.8003840776747291 134
 
11.3%
2.2932634702028287 107
 
9.0%
1.546823773938779 84
 
7.1%

Most occurring characters

ValueCountFrequency (%)
3 3039
13.7%
7 2660
12.0%
0 2636
11.9%
4 2345
10.6%
9 2139
9.6%
2 1725
7.8%
5 1658
7.5%
8 1515
6.8%
1 1395
6.3%
. 1190
 
5.4%
Other values (2) 1899
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 3039
13.7%
7 2660
12.0%
0 2636
11.9%
4 2345
10.6%
9 2139
9.6%
2 1725
7.8%
5 1658
7.5%
8 1515
6.8%
1 1395
6.3%
. 1190
 
5.4%
Other values (2) 1899
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 3039
13.7%
7 2660
12.0%
0 2636
11.9%
4 2345
10.6%
9 2139
9.6%
2 1725
7.8%
5 1658
7.5%
8 1515
6.8%
1 1395
6.3%
. 1190
 
5.4%
Other values (2) 1899
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 3039
13.7%
7 2660
12.0%
0 2636
11.9%
4 2345
10.6%
9 2139
9.6%
2 1725
7.8%
5 1658
7.5%
8 1515
6.8%
1 1395
6.3%
. 1190
 
5.4%
Other values (2) 1899
8.6%

post_im
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:13.269151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:13.400694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

IM_PG_P
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
-0.14346842995764317
1166 
6.970174555442163
 
24

Length

Max length20
Median length20
Mean length19.939496
Min length17

Characters and Unicode

Total characters23728
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.14346842995764317
2nd row-0.14346842995764317
3rd row-0.14346842995764317
4th row-0.14346842995764317
5th row-0.14346842995764317

Common Values

ValueCountFrequency (%)
-0.14346842995764317 1166
98.0%
6.970174555442163 24
 
2.0%

Length

2024-05-30T17:23:13.558713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:14.031169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.14346842995764317 1166
98.0%
6.970174555442163 24
 
2.0%

Most occurring characters

ValueCountFrequency (%)
4 4736
20.0%
1 2380
10.0%
6 2380
10.0%
7 2380
10.0%
3 2356
9.9%
9 2356
9.9%
5 1238
 
5.2%
0 1190
 
5.0%
. 1190
 
5.0%
2 1190
 
5.0%
Other values (2) 2332
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4736
20.0%
1 2380
10.0%
6 2380
10.0%
7 2380
10.0%
3 2356
9.9%
9 2356
9.9%
5 1238
 
5.2%
0 1190
 
5.0%
. 1190
 
5.0%
2 1190
 
5.0%
Other values (2) 2332
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4736
20.0%
1 2380
10.0%
6 2380
10.0%
7 2380
10.0%
3 2356
9.9%
9 2356
9.9%
5 1238
 
5.2%
0 1190
 
5.0%
. 1190
 
5.0%
2 1190
 
5.0%
Other values (2) 2332
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4736
20.0%
1 2380
10.0%
6 2380
10.0%
7 2380
10.0%
3 2356
9.9%
9 2356
9.9%
5 1238
 
5.2%
0 1190
 
5.0%
. 1190
 
5.0%
2 1190
 
5.0%
Other values (2) 2332
9.8%

n_r_ecg_p_01
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
-0.18889998090267365
1149 
5.293806781882244
 
41

Length

Max length20
Median length20
Mean length19.896639
Min length17

Characters and Unicode

Total characters23677
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.18889998090267365
2nd row-0.18889998090267365
3rd row-0.18889998090267365
4th row-0.18889998090267365
5th row-0.18889998090267365

Common Values

ValueCountFrequency (%)
-0.18889998090267365 1149
96.6%
5.293806781882244 41
 
3.4%

Length

2024-05-30T17:23:14.214296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:14.398841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.18889998090267365 1149
96.6%
5.293806781882244 41
 
3.4%

Most occurring characters

ValueCountFrequency (%)
8 4760
20.1%
9 4637
19.6%
0 3488
14.7%
6 2339
9.9%
2 1272
 
5.4%
. 1190
 
5.0%
1 1190
 
5.0%
7 1190
 
5.0%
3 1190
 
5.0%
5 1190
 
5.0%
Other values (2) 1231
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 4760
20.1%
9 4637
19.6%
0 3488
14.7%
6 2339
9.9%
2 1272
 
5.4%
. 1190
 
5.0%
1 1190
 
5.0%
7 1190
 
5.0%
3 1190
 
5.0%
5 1190
 
5.0%
Other values (2) 1231
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 4760
20.1%
9 4637
19.6%
0 3488
14.7%
6 2339
9.9%
2 1272
 
5.4%
. 1190
 
5.0%
1 1190
 
5.0%
7 1190
 
5.0%
3 1190
 
5.0%
5 1190
 
5.0%
Other values (2) 1231
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 4760
20.1%
9 4637
19.6%
0 3488
14.7%
6 2339
9.9%
2 1272
 
5.4%
. 1190
 
5.0%
1 1190
 
5.0%
7 1190
 
5.0%
3 1190
 
5.0%
5 1190
 
5.0%
Other values (2) 1231
 
5.2%

n_r_ecg_p_02
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.06495698024616309
1185 
15.39480431834065
 
5

Length

Max length20
Median length20
Mean length19.987395
Min length17

Characters and Unicode

Total characters23785
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.06495698024616309
2nd row-0.06495698024616309
3rd row-0.06495698024616309
4th row-0.06495698024616309
5th row-0.06495698024616309

Common Values

ValueCountFrequency (%)
-0.06495698024616309 1185
99.6%
15.39480431834065 5
 
0.4%

Length

2024-05-30T17:23:14.590559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:14.759064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06495698024616309 1185
99.6%
15.39480431834065 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

n_r_ecg_p_03
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.3 KiB
-0.38698637457445484
1035 
2.5840703076423273
155 

Length

Max length20
Median length20
Mean length19.739496
Min length18

Characters and Unicode

Total characters23490
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.38698637457445484
2nd row-0.38698637457445484
3rd row2.5840703076423273
4th row-0.38698637457445484
5th row-0.38698637457445484

Common Values

ValueCountFrequency (%)
-0.38698637457445484 1035
87.0%
2.5840703076423273 155
 
13.0%

Length

2024-05-30T17:23:14.957286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:15.139283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.38698637457445484 1035
87.0%
2.5840703076423273 155
 
13.0%

Most occurring characters

ValueCountFrequency (%)
4 5485
23.4%
8 3260
13.9%
3 2535
10.8%
7 2535
10.8%
6 2225
9.5%
5 2225
9.5%
0 1500
 
6.4%
. 1190
 
5.1%
- 1035
 
4.4%
9 1035
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 5485
23.4%
8 3260
13.9%
3 2535
10.8%
7 2535
10.8%
6 2225
9.5%
5 2225
9.5%
0 1500
 
6.4%
. 1190
 
5.1%
- 1035
 
4.4%
9 1035
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 5485
23.4%
8 3260
13.9%
3 2535
10.8%
7 2535
10.8%
6 2225
9.5%
5 2225
9.5%
0 1500
 
6.4%
. 1190
 
5.1%
- 1035
 
4.4%
9 1035
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 5485
23.4%
8 3260
13.9%
3 2535
10.8%
7 2535
10.8%
6 2225
9.5%
5 2225
9.5%
0 1500
 
6.4%
. 1190
 
5.1%
- 1035
 
4.4%
9 1035
 
4.4%

n_r_ecg_p_04
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.2072312362460679
1141 
4.825527358301295
 
49

Length

Max length19
Median length19
Mean length18.917647
Min length17

Characters and Unicode

Total characters22512
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.2072312362460679
2nd row4.825527358301295
3rd row-0.2072312362460679
4th row-0.2072312362460679
5th row-0.2072312362460679

Common Values

ValueCountFrequency (%)
-0.2072312362460679 1141
95.9%
4.825527358301295 49
 
4.1%

Length

2024-05-30T17:23:15.354158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:15.512406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2072312362460679 1141
95.9%
4.825527358301295 49
 
4.1%

Most occurring characters

ValueCountFrequency (%)
2 4711
20.9%
0 3472
15.4%
6 3423
15.2%
3 2380
10.6%
7 2331
10.4%
. 1190
 
5.3%
1 1190
 
5.3%
4 1190
 
5.3%
9 1190
 
5.3%
- 1141
 
5.1%
Other values (2) 294
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4711
20.9%
0 3472
15.4%
6 3423
15.2%
3 2380
10.6%
7 2331
10.4%
. 1190
 
5.3%
1 1190
 
5.3%
4 1190
 
5.3%
9 1190
 
5.3%
- 1141
 
5.1%
Other values (2) 294
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4711
20.9%
0 3472
15.4%
6 3423
15.2%
3 2380
10.6%
7 2331
10.4%
. 1190
 
5.3%
1 1190
 
5.3%
4 1190
 
5.3%
9 1190
 
5.3%
- 1141
 
5.1%
Other values (2) 294
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4711
20.9%
0 3472
15.4%
6 3423
15.2%
3 2380
10.6%
7 2331
10.4%
. 1190
 
5.3%
1 1190
 
5.3%
4 1190
 
5.3%
9 1190
 
5.3%
- 1141
 
5.1%
Other values (2) 294
 
1.3%

n_r_ecg_p_05
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.1608168802256692
1160 
6.218252702059209
 
30

Length

Max length19
Median length19
Mean length18.94958
Min length17

Characters and Unicode

Total characters22550
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.218252702059209
2nd row-0.1608168802256692
3rd row-0.1608168802256692
4th row-0.1608168802256692
5th row-0.1608168802256692

Common Values

ValueCountFrequency (%)
-0.1608168802256692 1160
97.5%
6.218252702059209 30
 
2.5%

Length

2024-05-30T17:23:15.710212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:15.902690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1608168802256692 1160
97.5%
6.218252702059209 30
 
2.5%

Most occurring characters

ValueCountFrequency (%)
6 4670
20.7%
2 3630
16.1%
0 3570
15.8%
8 3510
15.6%
1 2350
10.4%
5 1220
 
5.4%
9 1220
 
5.4%
. 1190
 
5.3%
- 1160
 
5.1%
7 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 4670
20.7%
2 3630
16.1%
0 3570
15.8%
8 3510
15.6%
1 2350
10.4%
5 1220
 
5.4%
9 1220
 
5.4%
. 1190
 
5.3%
- 1160
 
5.1%
7 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 4670
20.7%
2 3630
16.1%
0 3570
15.8%
8 3510
15.6%
1 2350
10.4%
5 1220
 
5.4%
9 1220
 
5.4%
. 1190
 
5.3%
- 1160
 
5.1%
7 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 4670
20.7%
2 3630
16.1%
0 3570
15.8%
8 3510
15.6%
1 2350
10.4%
5 1220
 
5.4%
9 1220
 
5.4%
. 1190
 
5.3%
- 1160
 
5.1%
7 30
 
0.1%

n_r_ecg_p_06
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.1167416810835558
1174 
8.565920849505906
 
16

Length

Max length19
Median length19
Mean length18.973109
Min length17

Characters and Unicode

Total characters22578
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1167416810835558
2nd row-0.1167416810835558
3rd row-0.1167416810835558
4th row-0.1167416810835558
5th row-0.1167416810835558

Common Values

ValueCountFrequency (%)
-0.1167416810835558 1174
98.7%
8.565920849505906 16
 
1.3%

Length

2024-05-30T17:23:16.100188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:16.286784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1167416810835558 1174
98.7%
8.565920849505906 16
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 4696
20.8%
5 3586
15.9%
8 3554
15.7%
0 2396
10.6%
6 2380
10.5%
. 1190
 
5.3%
4 1190
 
5.3%
- 1174
 
5.2%
7 1174
 
5.2%
3 1174
 
5.2%
Other values (2) 64
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4696
20.8%
5 3586
15.9%
8 3554
15.7%
0 2396
10.6%
6 2380
10.5%
. 1190
 
5.3%
4 1190
 
5.3%
- 1174
 
5.2%
7 1174
 
5.2%
3 1174
 
5.2%
Other values (2) 64
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4696
20.8%
5 3586
15.9%
8 3554
15.7%
0 2396
10.6%
6 2380
10.5%
. 1190
 
5.3%
4 1190
 
5.3%
- 1174
 
5.2%
7 1174
 
5.2%
3 1174
 
5.2%
Other values (2) 64
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4696
20.8%
5 3586
15.9%
8 3554
15.7%
0 2396
10.6%
6 2380
10.5%
. 1190
 
5.3%
4 1190
 
5.3%
- 1174
 
5.2%
7 1174
 
5.2%
3 1174
 
5.2%
Other values (2) 64
 
0.3%

n_r_ecg_p_08
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size90.8 KiB
-0.050273053910145554
1187 
19.891371663780923
 
3

Length

Max length21
Median length21
Mean length20.992437
Min length18

Characters and Unicode

Total characters24981
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.050273053910145554
2nd row-0.050273053910145554
3rd row-0.050273053910145554
4th row-0.050273053910145554
5th row-0.050273053910145554

Common Values

ValueCountFrequency (%)
-0.050273053910145554 1187
99.7%
19.891371663780923 3
 
0.3%

Length

2024-05-30T17:23:16.470298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:16.645548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.050273053910145554 1187
99.7%
19.891371663780923 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

n_r_ecg_p_09
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size90.8 KiB
-0.029000739528287082
1189 
34.48187929913334
 
1

Length

Max length21
Median length21
Mean length20.996639
Min length17

Characters and Unicode

Total characters24986
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row-0.029000739528287082
2nd row-0.029000739528287082
3rd row-0.029000739528287082
4th row-0.029000739528287082
5th row-0.029000739528287082

Common Values

ValueCountFrequency (%)
-0.029000739528287082 1189
99.9%
34.48187929913334 1
 
0.1%

Length

2024-05-30T17:23:16.855450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:16.997717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.029000739528287082 1189
99.9%
34.48187929913334 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7134
28.6%
2 4757
19.0%
8 3569
14.3%
9 2381
 
9.5%
7 2379
 
9.5%
3 1193
 
4.8%
. 1190
 
4.8%
- 1189
 
4.8%
5 1189
 
4.8%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7134
28.6%
2 4757
19.0%
8 3569
14.3%
9 2381
 
9.5%
7 2379
 
9.5%
3 1193
 
4.8%
. 1190
 
4.8%
- 1189
 
4.8%
5 1189
 
4.8%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7134
28.6%
2 4757
19.0%
8 3569
14.3%
9 2381
 
9.5%
7 2379
 
9.5%
3 1193
 
4.8%
. 1190
 
4.8%
- 1189
 
4.8%
5 1189
 
4.8%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7134
28.6%
2 4757
19.0%
8 3569
14.3%
9 2381
 
9.5%
7 2379
 
9.5%
3 1193
 
4.8%
. 1190
 
4.8%
- 1189
 
4.8%
5 1189
 
4.8%
4 3
 
< 0.1%

n_r_ecg_p_10
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.04103049699311091
1188 
24.37211521390788
 
2

Length

Max length20
Median length20
Mean length19.994958
Min length17

Characters and Unicode

Total characters23794
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04103049699311091
2nd row-0.04103049699311091
3rd row-0.04103049699311091
4th row-0.04103049699311091
5th row-0.04103049699311091

Common Values

ValueCountFrequency (%)
-0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Length

2024-05-30T17:23:17.144930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:17.272237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

n_p_ecg_p_01
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.04103049699311091
1188 
24.37211521390788
 
2

Length

Max length20
Median length20
Mean length19.994958
Min length17

Characters and Unicode

Total characters23794
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04103049699311091
2nd row-0.04103049699311091
3rd row-0.04103049699311091
4th row-0.04103049699311091
5th row-0.04103049699311091

Common Values

ValueCountFrequency (%)
-0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Length

2024-05-30T17:23:17.412002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:17.541758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

n_p_ecg_p_03
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.11674168108355581
1174 
8.565920849505908
 
16

Length

Max length20
Median length20
Mean length19.959664
Min length17

Characters and Unicode

Total characters23752
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.11674168108355581
2nd row-0.11674168108355581
3rd row-0.11674168108355581
4th row-0.11674168108355581
5th row-0.11674168108355581

Common Values

ValueCountFrequency (%)
-0.11674168108355581 1174
98.7%
8.565920849505908 16
 
1.3%

Length

2024-05-30T17:23:17.682939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:17.810576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.11674168108355581 1174
98.7%
8.565920849505908 16
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 5870
24.7%
5 3586
15.1%
8 3570
15.0%
0 2396
10.1%
6 2364
10.0%
. 1190
 
5.0%
4 1190
 
5.0%
- 1174
 
4.9%
7 1174
 
4.9%
3 1174
 
4.9%
Other values (2) 64
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5870
24.7%
5 3586
15.1%
8 3570
15.0%
0 2396
10.1%
6 2364
10.0%
. 1190
 
5.0%
4 1190
 
5.0%
- 1174
 
4.9%
7 1174
 
4.9%
3 1174
 
4.9%
Other values (2) 64
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5870
24.7%
5 3586
15.1%
8 3570
15.0%
0 2396
10.1%
6 2364
10.0%
. 1190
 
5.0%
4 1190
 
5.0%
- 1174
 
4.9%
7 1174
 
4.9%
3 1174
 
4.9%
Other values (2) 64
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5870
24.7%
5 3586
15.1%
8 3570
15.0%
0 2396
10.1%
6 2364
10.0%
. 1190
 
5.0%
4 1190
 
5.0%
- 1174
 
4.9%
7 1174
 
4.9%
3 1174
 
4.9%
Other values (2) 64
 
0.3%

n_p_ecg_p_04
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.04103049699311091
1188 
24.37211521390788
 
2

Length

Max length20
Median length20
Mean length19.994958
Min length17

Characters and Unicode

Total characters23794
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.04103049699311091
2nd row-0.04103049699311091
3rd row-0.04103049699311091
4th row-0.04103049699311091
5th row-0.04103049699311091

Common Values

ValueCountFrequency (%)
-0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Length

2024-05-30T17:23:17.960540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:18.098357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04103049699311091 1188
99.8%
24.37211521390788 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5942
25.0%
1 4758
20.0%
9 4754
20.0%
3 2380
10.0%
4 2378
10.0%
. 1190
 
5.0%
- 1188
 
5.0%
6 1188
 
5.0%
2 6
 
< 0.1%
7 4
 
< 0.1%
Other values (2) 6
 
< 0.1%

n_p_ecg_p_05
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.02900073952828708
1189 
34.48187929913333
 
1

Length

Max length20
Median length20
Mean length19.997479
Min length17

Characters and Unicode

Total characters23797
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row-0.02900073952828708
2nd row-0.02900073952828708
3rd row-0.02900073952828708
4th row-0.02900073952828708
5th row-0.02900073952828708

Common Values

ValueCountFrequency (%)
-0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Length

2024-05-30T17:23:18.255030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:18.391172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

n_p_ecg_p_06
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.08226900697897273
1182 
12.155245781143218
 
8

Length

Max length20
Median length20
Mean length19.986555
Min length18

Characters and Unicode

Total characters23784
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.08226900697897273
2nd row-0.08226900697897273
3rd row-0.08226900697897273
4th row-0.08226900697897273
5th row-0.08226900697897273

Common Values

ValueCountFrequency (%)
-0.08226900697897273 1182
99.3%
12.155245781143218 8
 
0.7%

Length

2024-05-30T17:23:18.546669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:18.696617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.08226900697897273 1182
99.3%
12.155245781143218 8
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 4728
19.9%
2 3570
15.0%
7 3554
14.9%
9 3546
14.9%
8 2380
10.0%
6 2364
9.9%
. 1190
 
5.0%
3 1190
 
5.0%
- 1182
 
5.0%
1 40
 
0.2%
Other values (2) 40
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4728
19.9%
2 3570
15.0%
7 3554
14.9%
9 3546
14.9%
8 2380
10.0%
6 2364
9.9%
. 1190
 
5.0%
3 1190
 
5.0%
- 1182
 
5.0%
1 40
 
0.2%
Other values (2) 40
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4728
19.9%
2 3570
15.0%
7 3554
14.9%
9 3546
14.9%
8 2380
10.0%
6 2364
9.9%
. 1190
 
5.0%
3 1190
 
5.0%
- 1182
 
5.0%
1 40
 
0.2%
Other values (2) 40
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4728
19.9%
2 3570
15.0%
7 3554
14.9%
9 3546
14.9%
8 2380
10.0%
6 2364
9.9%
. 1190
 
5.0%
3 1190
 
5.0%
- 1182
 
5.0%
1 40
 
0.2%
Other values (2) 40
 
0.2%

n_p_ecg_p_07
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
-0.24618298195866542
1122 
4.0620192023179795
 
68

Length

Max length20
Median length20
Mean length19.885714
Min length18

Characters and Unicode

Total characters23664
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.24618298195866542
2nd row-0.24618298195866542
3rd row-0.24618298195866542
4th row-0.24618298195866542
5th row-0.24618298195866542

Common Values

ValueCountFrequency (%)
-0.24618298195866542 1122
94.3%
4.0620192023179795 68
 
5.7%

Length

2024-05-30T17:23:18.848396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:18.989646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.24618298195866542 1122
94.3%
4.0620192023179795 68
 
5.7%

Most occurring characters

ValueCountFrequency (%)
2 3570
15.1%
6 3434
14.5%
8 3366
14.2%
9 2448
10.3%
1 2380
10.1%
4 2312
9.8%
5 2312
9.8%
0 1326
 
5.6%
. 1190
 
5.0%
- 1122
 
4.7%
Other values (2) 204
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3570
15.1%
6 3434
14.5%
8 3366
14.2%
9 2448
10.3%
1 2380
10.1%
4 2312
9.8%
5 2312
9.8%
0 1326
 
5.6%
. 1190
 
5.0%
- 1122
 
4.7%
Other values (2) 204
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3570
15.1%
6 3434
14.5%
8 3366
14.2%
9 2448
10.3%
1 2380
10.1%
4 2312
9.8%
5 2312
9.8%
0 1326
 
5.6%
. 1190
 
5.0%
- 1122
 
4.7%
Other values (2) 204
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3570
15.1%
6 3434
14.5%
8 3366
14.2%
9 2448
10.3%
1 2380
10.1%
4 2312
9.8%
5 2312
9.8%
0 1326
 
5.6%
. 1190
 
5.0%
- 1122
 
4.7%
Other values (2) 204
 
0.9%

n_p_ecg_p_08
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.0580747904139041
1186 
17.219175357722563
 
4

Length

Max length19
Median length19
Mean length18.996639
Min length18

Characters and Unicode

Total characters22606
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17.219175357722563
2nd row-0.0580747904139041
3rd row-0.0580747904139041
4th row-0.0580747904139041
5th row-0.0580747904139041

Common Values

ValueCountFrequency (%)
-0.0580747904139041 1186
99.7%
17.219175357722563 4
 
0.3%

Length

2024-05-30T17:23:19.131924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:19.257146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0580747904139041 1186
99.7%
17.219175357722563 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 5930
26.2%
4 3558
15.7%
7 2388
10.6%
1 2384
10.5%
9 2376
10.5%
5 1198
 
5.3%
3 1194
 
5.3%
. 1190
 
5.3%
- 1186
 
5.2%
8 1186
 
5.2%
Other values (2) 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5930
26.2%
4 3558
15.7%
7 2388
10.6%
1 2384
10.5%
9 2376
10.5%
5 1198
 
5.3%
3 1194
 
5.3%
. 1190
 
5.3%
- 1186
 
5.2%
8 1186
 
5.2%
Other values (2) 16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5930
26.2%
4 3558
15.7%
7 2388
10.6%
1 2384
10.5%
9 2376
10.5%
5 1198
 
5.3%
3 1194
 
5.3%
. 1190
 
5.3%
- 1186
 
5.2%
8 1186
 
5.2%
Other values (2) 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5930
26.2%
4 3558
15.7%
7 2388
10.6%
1 2384
10.5%
9 2376
10.5%
5 1198
 
5.3%
3 1194
 
5.3%
. 1190
 
5.3%
- 1186
 
5.2%
8 1186
 
5.2%
Other values (2) 16
 
0.1%

n_p_ecg_p_09
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.08226900697897273
1182 
12.155245781143218
 
8

Length

Max length20
Median length20
Mean length19.986555
Min length18

Characters and Unicode

Total characters23784
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.08226900697897273
2nd row-0.08226900697897273
3rd row-0.08226900697897273
4th row-0.08226900697897273
5th row-0.08226900697897273

Common Values

ValueCountFrequency (%)
-0.08226900697897273 1182
99.3%
12.155245781143218 8
 
0.7%

Length

2024-05-30T17:23:19.410844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:19.558661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.08226900697897273 1182
99.3%
12.155245781143218 8
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 4728
19.9%
2 3570
15.0%
7 3554
14.9%
9 3546
14.9%
8 2380
10.0%
6 2364
9.9%
. 1190
 
5.0%
3 1190
 
5.0%
- 1182
 
5.0%
1 40
 
0.2%
Other values (2) 40
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4728
19.9%
2 3570
15.0%
7 3554
14.9%
9 3546
14.9%
8 2380
10.0%
6 2364
9.9%
. 1190
 
5.0%
3 1190
 
5.0%
- 1182
 
5.0%
1 40
 
0.2%
Other values (2) 40
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4728
19.9%
2 3570
15.0%
7 3554
14.9%
9 3546
14.9%
8 2380
10.0%
6 2364
9.9%
. 1190
 
5.0%
3 1190
 
5.0%
- 1182
 
5.0%
1 40
 
0.2%
Other values (2) 40
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4728
19.9%
2 3570
15.0%
7 3554
14.9%
9 3546
14.9%
8 2380
10.0%
6 2364
9.9%
. 1190
 
5.0%
3 1190
 
5.0%
- 1182
 
5.0%
1 40
 
0.2%
Other values (2) 40
 
0.2%

n_p_ecg_p_10
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.12392885771580416
1172 
8.069145624606804
 
18

Length

Max length20
Median length20
Mean length19.954622
Min length17

Characters and Unicode

Total characters23746
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.12392885771580416
2nd row-0.12392885771580416
3rd row-0.12392885771580416
4th row-0.12392885771580416
5th row-0.12392885771580416

Common Values

ValueCountFrequency (%)
-0.12392885771580416 1172
98.5%
8.069145624606804 18
 
1.5%

Length

2024-05-30T17:23:19.712121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:19.861351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.12392885771580416 1172
98.5%
8.069145624606804 18
 
1.5%

Most occurring characters

ValueCountFrequency (%)
8 3552
15.0%
1 3534
14.9%
0 2398
10.1%
2 2362
9.9%
5 2362
9.9%
7 2344
9.9%
6 1244
 
5.2%
4 1226
 
5.2%
. 1190
 
5.0%
9 1190
 
5.0%
Other values (2) 2344
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 3552
15.0%
1 3534
14.9%
0 2398
10.1%
2 2362
9.9%
5 2362
9.9%
7 2344
9.9%
6 1244
 
5.2%
4 1226
 
5.2%
. 1190
 
5.0%
9 1190
 
5.0%
Other values (2) 2344
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 3552
15.0%
1 3534
14.9%
0 2398
10.1%
2 2362
9.9%
5 2362
9.9%
7 2344
9.9%
6 1244
 
5.2%
4 1226
 
5.2%
. 1190
 
5.0%
9 1190
 
5.0%
Other values (2) 2344
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 3552
15.0%
1 3534
14.9%
0 2398
10.1%
2 2362
9.9%
5 2362
9.9%
7 2344
9.9%
6 1244
 
5.2%
4 1226
 
5.2%
. 1190
 
5.0%
9 1190
 
5.0%
Other values (2) 2344
9.9%

n_p_ecg_p_11
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
-0.13724291033913613
1168 
7.286350876186864
 
22

Length

Max length20
Median length20
Mean length19.944538
Min length17

Characters and Unicode

Total characters23734
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.13724291033913613
2nd row-0.13724291033913613
3rd row-0.13724291033913613
4th row-0.13724291033913613
5th row-0.13724291033913613

Common Values

ValueCountFrequency (%)
-0.13724291033913613 1168
98.2%
7.286350876186864 22
 
1.8%

Length

2024-05-30T17:23:20.006602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:20.142603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.13724291033913613 1168
98.2%
7.286350876186864 22
 
1.8%

Most occurring characters

ValueCountFrequency (%)
3 5862
24.7%
1 4694
19.8%
0 2358
9.9%
2 2358
9.9%
9 2336
 
9.8%
6 1256
 
5.3%
7 1212
 
5.1%
. 1190
 
5.0%
4 1190
 
5.0%
- 1168
 
4.9%
Other values (2) 110
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 5862
24.7%
1 4694
19.8%
0 2358
9.9%
2 2358
9.9%
9 2336
 
9.8%
6 1256
 
5.3%
7 1212
 
5.1%
. 1190
 
5.0%
4 1190
 
5.0%
- 1168
 
4.9%
Other values (2) 110
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 5862
24.7%
1 4694
19.8%
0 2358
9.9%
2 2358
9.9%
9 2336
 
9.8%
6 1256
 
5.3%
7 1212
 
5.1%
. 1190
 
5.0%
4 1190
 
5.0%
- 1168
 
4.9%
Other values (2) 110
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 5862
24.7%
1 4694
19.8%
0 2358
9.9%
2 2358
9.9%
9 2336
 
9.8%
6 1256
 
5.3%
7 1212
 
5.1%
. 1190
 
5.0%
4 1190
 
5.0%
- 1168
 
4.9%
Other values (2) 110
 
0.5%

n_p_ecg_p_12
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.1840749290264439
1151 
5.432570341267613
 
39

Length

Max length19
Median length19
Mean length18.934454
Min length17

Characters and Unicode

Total characters22532
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1840749290264439
2nd row-0.1840749290264439
3rd row-0.1840749290264439
4th row-0.1840749290264439
5th row-0.1840749290264439

Common Values

ValueCountFrequency (%)
-0.1840749290264439 1151
96.7%
5.432570341267613 39
 
3.3%

Length

2024-05-30T17:23:20.303602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:20.451790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1840749290264439 1151
96.7%
5.432570341267613 39
 
3.3%

Most occurring characters

ValueCountFrequency (%)
4 4682
20.8%
0 3492
15.5%
9 3453
15.3%
2 2380
10.6%
3 1268
 
5.6%
1 1229
 
5.5%
7 1229
 
5.5%
6 1229
 
5.5%
. 1190
 
5.3%
- 1151
 
5.1%
Other values (2) 1229
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4682
20.8%
0 3492
15.5%
9 3453
15.3%
2 2380
10.6%
3 1268
 
5.6%
1 1229
 
5.5%
7 1229
 
5.5%
6 1229
 
5.5%
. 1190
 
5.3%
- 1151
 
5.1%
Other values (2) 1229
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4682
20.8%
0 3492
15.5%
9 3453
15.3%
2 2380
10.6%
3 1268
 
5.6%
1 1229
 
5.5%
7 1229
 
5.5%
6 1229
 
5.5%
. 1190
 
5.3%
- 1151
 
5.1%
Other values (2) 1229
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4682
20.8%
0 3492
15.5%
9 3453
15.3%
2 2380
10.6%
3 1268
 
5.6%
1 1229
 
5.5%
7 1229
 
5.5%
6 1229
 
5.5%
. 1190
 
5.3%
- 1151
 
5.1%
Other values (2) 1229
 
5.5%

fibr_ter_01
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.09205746178983233
1180 
10.862780491200215
 
10

Length

Max length20
Median length20
Mean length19.983193
Min length18

Characters and Unicode

Total characters23780
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.09205746178983233
2nd row-0.09205746178983233
3rd row-0.09205746178983233
4th row-0.09205746178983233
5th row-0.09205746178983233

Common Values

ValueCountFrequency (%)
-0.09205746178983233 1180
99.2%
10.862780491200215 10
 
0.8%

Length

2024-05-30T17:23:20.616251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:20.754078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.09205746178983233 1180
99.2%
10.862780491200215 10
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 3580
15.1%
3 3540
14.9%
2 2390
10.1%
8 2380
10.0%
9 2370
10.0%
7 2370
10.0%
1 1210
 
5.1%
. 1190
 
5.0%
5 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2370
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23780
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3580
15.1%
3 3540
14.9%
2 2390
10.1%
8 2380
10.0%
9 2370
10.0%
7 2370
10.0%
1 1210
 
5.1%
. 1190
 
5.0%
5 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2370
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23780
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3580
15.1%
3 3540
14.9%
2 2390
10.1%
8 2380
10.0%
9 2370
10.0%
7 2370
10.0%
1 1210
 
5.1%
. 1190
 
5.0%
5 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2370
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23780
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3580
15.1%
3 3540
14.9%
2 2390
10.1%
8 2380
10.0%
9 2370
10.0%
7 2370
10.0%
1 1210
 
5.1%
. 1190
 
5.0%
5 1190
 
5.0%
4 1190
 
5.0%
Other values (2) 2370
10.0%

fibr_ter_02
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.07118684968143742
1184 
14.047538337136983
 
6

Length

Max length20
Median length20
Mean length19.989916
Min length18

Characters and Unicode

Total characters23788
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.07118684968143742
2nd row-0.07118684968143742
3rd row-0.07118684968143742
4th row-0.07118684968143742
5th row-0.07118684968143742

Common Values

ValueCountFrequency (%)
-0.07118684968143742 1184
99.5%
14.047538337136983 6
 
0.5%

Length

2024-05-30T17:23:20.905941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:21.044450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07118684968143742 1184
99.5%
14.047538337136983 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

fibr_ter_03
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.1912730139190015
1148 
5.228129047119375
 
42

Length

Max length19
Median length19
Mean length18.929412
Min length17

Characters and Unicode

Total characters22526
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1912730139190015
2nd row-0.1912730139190015
3rd row-0.1912730139190015
4th row-0.1912730139190015
5th row-0.1912730139190015

Common Values

ValueCountFrequency (%)
-0.1912730139190015 1148
96.5%
5.228129047119375 42
 
3.5%

Length

2024-05-30T17:23:21.202410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:21.352411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1912730139190015 1148
96.5%
5.228129047119375 42
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 5866
26.0%
0 4634
20.6%
9 3528
15.7%
3 2338
 
10.4%
2 1274
 
5.7%
7 1232
 
5.5%
5 1232
 
5.5%
. 1190
 
5.3%
- 1148
 
5.1%
8 42
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22526
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5866
26.0%
0 4634
20.6%
9 3528
15.7%
3 2338
 
10.4%
2 1274
 
5.7%
7 1232
 
5.5%
5 1232
 
5.5%
. 1190
 
5.3%
- 1148
 
5.1%
8 42
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22526
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5866
26.0%
0 4634
20.6%
9 3528
15.7%
3 2338
 
10.4%
2 1274
 
5.7%
7 1232
 
5.5%
5 1232
 
5.5%
. 1190
 
5.3%
- 1148
 
5.1%
8 42
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22526
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5866
26.0%
0 4634
20.6%
9 3528
15.7%
3 2338
 
10.4%
2 1274
 
5.7%
7 1232
 
5.5%
5 1232
 
5.5%
. 1190
 
5.3%
- 1148
 
5.1%
8 42
 
0.2%

fibr_ter_05
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size90.8 KiB
-0.050273053910145554
1187 
19.891371663780923
 
3

Length

Max length21
Median length21
Mean length20.992437
Min length18

Characters and Unicode

Total characters24981
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.050273053910145554
2nd row-0.050273053910145554
3rd row-0.050273053910145554
4th row-0.050273053910145554
5th row-0.050273053910145554

Common Values

ValueCountFrequency (%)
-0.050273053910145554 1187
99.7%
19.891371663780923 3
 
0.3%

Length

2024-05-30T17:23:21.504187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:21.635841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.050273053910145554 1187
99.7%
19.891371663780923 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5938
23.8%
5 5935
23.8%
3 2383
9.5%
1 2383
9.5%
4 2374
 
9.5%
9 1196
 
4.8%
7 1193
 
4.8%
. 1190
 
4.8%
2 1190
 
4.8%
- 1187
 
4.8%
Other values (2) 12
 
< 0.1%

fibr_ter_06
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.07118684968143742
1184 
14.047538337136983
 
6

Length

Max length20
Median length20
Mean length19.989916
Min length18

Characters and Unicode

Total characters23788
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.07118684968143742
2nd row-0.07118684968143742
3rd row-0.07118684968143742
4th row-0.07118684968143742
5th row-0.07118684968143742

Common Values

ValueCountFrequency (%)
-0.07118684968143742 1184
99.5%
14.047538337136983 6
 
0.5%

Length

2024-05-30T17:23:21.793793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:21.931793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.07118684968143742 1184
99.5%
14.047538337136983 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3564
15.0%
8 3564
15.0%
4 3564
15.0%
7 2380
10.0%
0 2374
10.0%
6 2374
10.0%
3 1214
 
5.1%
. 1190
 
5.0%
9 1190
 
5.0%
- 1184
 
5.0%
Other values (2) 1190
 
5.0%

fibr_ter_07
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.06495698024616309
1185 
15.39480431834065
 
5

Length

Max length20
Median length20
Mean length19.987395
Min length17

Characters and Unicode

Total characters23785
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.06495698024616309
2nd row-0.06495698024616309
3rd row-0.06495698024616309
4th row-0.06495698024616309
5th row-0.06495698024616309

Common Values

ValueCountFrequency (%)
-0.06495698024616309 1185
99.6%
15.39480431834065 5
 
0.4%

Length

2024-05-30T17:23:22.086794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:22.224792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.06495698024616309 1185
99.6%
15.39480431834065 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4750
20.0%
6 4745
19.9%
9 3560
15.0%
4 2385
10.0%
3 1200
 
5.0%
5 1195
 
5.0%
8 1195
 
5.0%
1 1195
 
5.0%
. 1190
 
5.0%
- 1185
 
5.0%

fibr_ter_08
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.6 KiB
-0.02900073952828708
1189 
34.48187929913333
 
1

Length

Max length20
Median length20
Mean length19.997479
Min length17

Characters and Unicode

Total characters23797
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row-0.02900073952828708
2nd row-0.02900073952828708
3rd row-0.02900073952828708
4th row-0.02900073952828708
5th row-0.02900073952828708

Common Values

ValueCountFrequency (%)
-0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Length

2024-05-30T17:23:22.373443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:22.501817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.02900073952828708 1189
99.9%
34.48187929913333 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7134
30.0%
8 3569
15.0%
2 3568
15.0%
9 2381
 
10.0%
7 2379
 
10.0%
3 1194
 
5.0%
. 1190
 
5.0%
- 1189
 
5.0%
5 1189
 
5.0%
4 2
 
< 0.1%

L_BLOOD
Real number (ℝ)

ZEROS 

Distinct125
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.4516866 × 10-16
Minimum-2.2595641
Maximum2.5848866
Zeros71
Zeros (%)6.0%
Negative656
Negative (%)55.1%
Memory size9.4 KiB
2024-05-30T17:23:22.649407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2.2595641
5-th percentile-1.3468415
Q1-0.71495663
median-0.15328118
Q30.51370841
95-th percentile2.1285253
Maximum2.5848866
Range4.8444507
Interquartile range (IQR)1.228665

Descriptive statistics

Standard deviation1.0004204
Coefficient of variation (CV)-6.8914354 × 1015
Kurtosis0.20466199
Mean-1.4516866 × 10-16
Median Absolute Deviation (MAD)0.63188488
Skewness0.78112863
Sum-1.9895197 × 10-13
Variance1.000841
MonotonicityNot monotonic
2024-05-30T17:23:22.839918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
6.0%
2.584886628 41
 
3.4%
-0.5745377653 24
 
2.0%
-0.4341189033 23
 
1.9%
-0.1532811795 23
 
1.9%
-0.8553754891 22
 
1.8%
-1.206422644 22
 
1.8%
-0.3990141879 21
 
1.8%
-0.7149566272 21
 
1.8%
-0.5043283343 21
 
1.8%
Other values (115) 901
75.7%
ValueCountFrequency (%)
-2.259564108 1
 
0.1%
-2.224459393 1
 
0.1%
-1.838307523 2
 
0.2%
-1.768098092 1
 
0.1%
-1.732993376 1
 
0.1%
-1.697888661 1
 
0.1%
-1.662783945 1
 
0.1%
-1.62767923 2
 
0.2%
-1.592574514 2
 
0.2%
-1.557469799 9
0.8%
ValueCountFrequency (%)
2.584886628 41
3.4%
2.549781913 2
 
0.2%
2.479572482 1
 
0.1%
2.444467766 3
 
0.3%
2.409363051 2
 
0.2%
2.374258335 1
 
0.1%
2.268944189 1
 
0.1%
2.233839473 2
 
0.2%
2.198734758 3
 
0.3%
2.163630042 3
 
0.3%

TIME_B_S
Real number (ℝ)

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.3434632 × 10-17
Minimum-1.2788338
Maximum1.4570084
Zeros0
Zeros (%)0.0%
Negative622
Negative (%)52.3%
Memory size9.4 KiB
2024-05-30T17:23:22.975895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.2788338
5-th percentile-1.2788338
Q1-0.93685356
median-0.25289298
Q31.1150282
95-th percentile1.4570084
Maximum1.4570084
Range2.7358423
Interquartile range (IQR)2.0518817

Descriptive statistics

Standard deviation1.0004204
Coefficient of variation (CV)-7.4465789 × 1016
Kurtosis-1.5180078
Mean-1.3434632 × 10-17
Median Absolute Deviation (MAD)0.68396057
Skewness0.25605813
Sum0
Variance1.000841
MonotonicityNot monotonic
2024-05-30T17:23:23.108193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
-0.9368535562 326
27.4%
1.457008445 223
18.7%
-1.278833842 124
 
10.4%
0.7730478731 117
 
9.8%
-0.5948732704 116
 
9.7%
0.4310675872 98
 
8.2%
1.115028159 76
 
6.4%
-0.2528929845 56
 
4.7%
0.08908730136 54
 
4.5%
ValueCountFrequency (%)
-1.278833842 124
 
10.4%
-0.9368535562 326
27.4%
-0.5948732704 116
 
9.7%
-0.2528929845 56
 
4.7%
0.08908730136 54
 
4.5%
0.4310675872 98
 
8.2%
0.7730478731 117
 
9.8%
1.115028159 76
 
6.4%
1.457008445 223
18.7%
ValueCountFrequency (%)
1.457008445 223
18.7%
1.115028159 76
 
6.4%
0.7730478731 117
 
9.8%
0.4310675872 98
 
8.2%
0.08908730136 54
 
4.5%
-0.2528929845 56
 
4.7%
-0.5948732704 116
 
9.7%
-0.9368535562 326
27.4%
-1.278833842 124
 
10.4%

R_AB_1_n
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:23.255761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:23.373711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

R_AB_2_n
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:23.503288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:23.615803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

R_AB_3_n
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:23.741569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:23.860735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

NITR_S
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.4 KiB
-0.2648469084057549
1112 
3.7757661813743524
 
78

Length

Max length19
Median length19
Mean length18.934454
Min length18

Characters and Unicode

Total characters22532
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.2648469084057549
2nd row-0.2648469084057549
3rd row-0.2648469084057549
4th row-0.2648469084057549
5th row-0.2648469084057549

Common Values

ValueCountFrequency (%)
-0.2648469084057549 1112
93.4%
3.7757661813743524 78
 
6.6%

Length

2024-05-30T17:23:23.991114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:24.117113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2648469084057549 1112
93.4%
3.7757661813743524 78
 
6.6%

Most occurring characters

ValueCountFrequency (%)
4 4604
20.4%
0 3336
14.8%
6 2380
10.6%
5 2380
10.6%
8 2302
10.2%
9 2224
9.9%
7 1424
 
6.3%
. 1190
 
5.3%
2 1190
 
5.3%
- 1112
 
4.9%
Other values (2) 390
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4604
20.4%
0 3336
14.8%
6 2380
10.6%
5 2380
10.6%
8 2302
10.2%
9 2224
9.9%
7 1424
 
6.3%
. 1190
 
5.3%
2 1190
 
5.3%
- 1112
 
4.9%
Other values (2) 390
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4604
20.4%
0 3336
14.8%
6 2380
10.6%
5 2380
10.6%
8 2302
10.2%
9 2224
9.9%
7 1424
 
6.3%
. 1190
 
5.3%
2 1190
 
5.3%
- 1112
 
4.9%
Other values (2) 390
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4604
20.4%
0 3336
14.8%
6 2380
10.6%
5 2380
10.6%
8 2302
10.2%
9 2224
9.9%
7 1424
 
6.3%
. 1190
 
5.3%
2 1190
 
5.3%
- 1112
 
4.9%
Other values (2) 390
 
1.7%

NA_R_1_n
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size88.0 KiB
-0.5875338126138688
839 
0.9558773066146167
259 
2.499288425843102
 
72
3.270993985457345
 
20

Length

Max length19
Median length19
Mean length18.627731
Min length17

Characters and Unicode

Total characters22167
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.5875338126138688
2nd row-0.5875338126138688
3rd row0.9558773066146167
4th row-0.5875338126138688
5th row-0.5875338126138688

Common Values

ValueCountFrequency (%)
-0.5875338126138688 839
70.5%
0.9558773066146167 259
 
21.8%
2.499288425843102 72
 
6.1%
3.270993985457345 20
 
1.7%

Length

2024-05-30T17:23:24.266590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:24.414480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.5875338126138688 839
70.5%
0.9558773066146167 259
 
21.8%
2.499288425843102 72
 
6.1%
3.270993985457345 20
 
1.7%

Most occurring characters

ValueCountFrequency (%)
8 4690
21.2%
3 2908
13.1%
6 2714
12.2%
5 2328
10.5%
1 2268
10.2%
7 1656
 
7.5%
0 1449
 
6.5%
. 1190
 
5.4%
2 1147
 
5.2%
- 839
 
3.8%
Other values (2) 978
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22167
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 4690
21.2%
3 2908
13.1%
6 2714
12.2%
5 2328
10.5%
1 2268
10.2%
7 1656
 
7.5%
0 1449
 
6.5%
. 1190
 
5.4%
2 1147
 
5.2%
- 839
 
3.8%
Other values (2) 978
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22167
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 4690
21.2%
3 2908
13.1%
6 2714
12.2%
5 2328
10.5%
1 2268
10.2%
7 1656
 
7.5%
0 1449
 
6.5%
. 1190
 
5.4%
2 1147
 
5.2%
- 839
 
3.8%
Other values (2) 978
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22167
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 4690
21.2%
3 2908
13.1%
6 2714
12.2%
5 2328
10.5%
1 2268
10.2%
7 1656
 
7.5%
0 1449
 
6.5%
. 1190
 
5.4%
2 1147
 
5.2%
- 839
 
3.8%
Other values (2) 978
 
4.4%

NA_R_2_n
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:24.563001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:24.676358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

NA_R_3_n
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:25.052520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:25.165809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

NOT_NA_1_n
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size88.1 KiB
-0.5619155459067758
865 
1.1548482528841442
269 
2.871612051675064
 
39
3.7299939510705236
 
17

Length

Max length19
Median length19
Mean length18.694118
Min length17

Characters and Unicode

Total characters22246
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.5619155459067758
2nd row1.1548482528841442
3rd row3.7299939510705236
4th row-0.5619155459067758
5th row-0.5619155459067758

Common Values

ValueCountFrequency (%)
-0.5619155459067758 865
72.7%
1.1548482528841442 269
 
22.6%
2.871612051675064 39
 
3.3%
3.7299939510705236 17
 
1.4%

Length

2024-05-30T17:23:25.302956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:25.445521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.5619155459067758 865
72.7%
1.1548482528841442 269
 
22.6%
2.871612051675064 39
 
3.3%
3.7299939510705236 17
 
1.4%

Most occurring characters

ValueCountFrequency (%)
5 4975
22.4%
1 2671
12.0%
4 2249
10.1%
8 1980
 
8.9%
6 1864
 
8.4%
0 1842
 
8.3%
7 1842
 
8.3%
9 1798
 
8.1%
. 1190
 
5.3%
2 919
 
4.1%
Other values (2) 916
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22246
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 4975
22.4%
1 2671
12.0%
4 2249
10.1%
8 1980
 
8.9%
6 1864
 
8.4%
0 1842
 
8.3%
7 1842
 
8.3%
9 1798
 
8.1%
. 1190
 
5.3%
2 919
 
4.1%
Other values (2) 916
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22246
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 4975
22.4%
1 2671
12.0%
4 2249
10.1%
8 1980
 
8.9%
6 1864
 
8.4%
0 1842
 
8.3%
7 1842
 
8.3%
9 1798
 
8.1%
. 1190
 
5.3%
2 919
 
4.1%
Other values (2) 916
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22246
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 4975
22.4%
1 2671
12.0%
4 2249
10.1%
8 1980
 
8.9%
6 1864
 
8.4%
0 1842
 
8.3%
7 1842
 
8.3%
9 1798
 
8.1%
. 1190
 
5.3%
2 919
 
4.1%
Other values (2) 916
 
4.1%

NOT_NA_2_n
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:25.593051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:25.704110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

NOT_NA_3_n
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.9 KiB
0.0
1190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1190
100.0%

Length

2024-05-30T17:23:25.826120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:25.950178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2380
66.7%
. 1190
33.3%

LID_S_n
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.1 KiB
-0.6246494343932848
856 
1.6008979516187178
334 

Length

Max length19
Median length19
Mean length18.719328
Min length18

Characters and Unicode

Total characters22276
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.6008979516187178
2nd row1.6008979516187178
3rd row1.6008979516187178
4th row-0.6246494343932848
5th row-0.6246494343932848

Common Values

ValueCountFrequency (%)
-0.6246494343932848 856
71.9%
1.6008979516187178 334
 
28.1%

Length

2024-05-30T17:23:26.075108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:26.198275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6246494343932848 856
71.9%
1.6008979516187178 334
 
28.1%

Most occurring characters

ValueCountFrequency (%)
4 4280
19.2%
8 2714
12.2%
3 2568
11.5%
6 2380
10.7%
9 2380
10.7%
2 1712
 
7.7%
0 1524
 
6.8%
1 1336
 
6.0%
. 1190
 
5.3%
7 1002
 
4.5%
Other values (2) 1190
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 4280
19.2%
8 2714
12.2%
3 2568
11.5%
6 2380
10.7%
9 2380
10.7%
2 1712
 
7.7%
0 1524
 
6.8%
1 1336
 
6.0%
. 1190
 
5.3%
7 1002
 
4.5%
Other values (2) 1190
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 4280
19.2%
8 2714
12.2%
3 2568
11.5%
6 2380
10.7%
9 2380
10.7%
2 1712
 
7.7%
0 1524
 
6.8%
1 1336
 
6.0%
. 1190
 
5.3%
7 1002
 
4.5%
Other values (2) 1190
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 4280
19.2%
8 2714
12.2%
3 2568
11.5%
6 2380
10.7%
9 2380
10.7%
2 1712
 
7.7%
0 1524
 
6.8%
1 1336
 
6.0%
. 1190
 
5.3%
7 1002
 
4.5%
Other values (2) 1190
 
5.3%

B_BLOK_S_n
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.1 KiB
-0.4040363310319307
1023 
2.475024949974043
167 

Length

Max length19
Median length19
Mean length18.719328
Min length17

Characters and Unicode

Total characters22276
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4040363310319307
2nd row-0.4040363310319307
3rd row2.475024949974043
4th row-0.4040363310319307
5th row-0.4040363310319307

Common Values

ValueCountFrequency (%)
-0.4040363310319307 1023
86.0%
2.475024949974043 167
 
14.0%

Length

2024-05-30T17:23:26.358034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:26.495142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.4040363310319307 1023
86.0%
2.475024949974043 167
 
14.0%

Most occurring characters

ValueCountFrequency (%)
0 5449
24.5%
3 5282
23.7%
4 2881
12.9%
1 2046
 
9.2%
9 1524
 
6.8%
7 1357
 
6.1%
. 1190
 
5.3%
- 1023
 
4.6%
6 1023
 
4.6%
2 334
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5449
24.5%
3 5282
23.7%
4 2881
12.9%
1 2046
 
9.2%
9 1524
 
6.8%
7 1357
 
6.1%
. 1190
 
5.3%
- 1023
 
4.6%
6 1023
 
4.6%
2 334
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5449
24.5%
3 5282
23.7%
4 2881
12.9%
1 2046
 
9.2%
9 1524
 
6.8%
7 1357
 
6.1%
. 1190
 
5.3%
- 1023
 
4.6%
6 1023
 
4.6%
2 334
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5449
24.5%
3 5282
23.7%
4 2881
12.9%
1 2046
 
9.2%
9 1524
 
6.8%
7 1357
 
6.1%
. 1190
 
5.3%
- 1023
 
4.6%
6 1023
 
4.6%
2 334
 
1.5%

ANT_CA_S_n
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size87.6 KiB
0.6311536077816129
851 
-1.5844003546376184
339 

Length

Max length19
Median length18
Mean length18.284874
Min length18

Characters and Unicode

Total characters21759
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.5844003546376184
2nd row0.6311536077816129
3rd row-1.5844003546376184
4th row0.6311536077816129
5th row0.6311536077816129

Common Values

ValueCountFrequency (%)
0.6311536077816129 851
71.5%
-1.5844003546376184 339
 
28.5%

Length

2024-05-30T17:23:26.629138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:26.751149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6311536077816129 851
71.5%
1.5844003546376184 339
 
28.5%

Most occurring characters

ValueCountFrequency (%)
1 4082
18.8%
6 3231
14.8%
0 2380
10.9%
3 2380
10.9%
7 2041
9.4%
5 1529
 
7.0%
8 1529
 
7.0%
4 1356
 
6.2%
. 1190
 
5.5%
2 851
 
3.9%
Other values (2) 1190
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4082
18.8%
6 3231
14.8%
0 2380
10.9%
3 2380
10.9%
7 2041
9.4%
5 1529
 
7.0%
8 1529
 
7.0%
4 1356
 
6.2%
. 1190
 
5.5%
2 851
 
3.9%
Other values (2) 1190
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4082
18.8%
6 3231
14.8%
0 2380
10.9%
3 2380
10.9%
7 2041
9.4%
5 1529
 
7.0%
8 1529
 
7.0%
4 1356
 
6.2%
. 1190
 
5.5%
2 851
 
3.9%
Other values (2) 1190
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4082
18.8%
6 3231
14.8%
0 2380
10.9%
3 2380
10.9%
7 2041
9.4%
5 1529
 
7.0%
8 1529
 
7.0%
4 1356
 
6.2%
. 1190
 
5.5%
2 851
 
3.9%
Other values (2) 1190
 
5.5%

GEPAR_S_n
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size87.6 KiB
0.6337578392589267
849 
-1.5778897522898205
341 

Length

Max length19
Median length18
Mean length18.286555
Min length18

Characters and Unicode

Total characters21761
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.6337578392589267
2nd row0.6337578392589267
3rd row0.6337578392589267
4th row0.6337578392589267
5th row-1.5778897522898205

Common Values

ValueCountFrequency (%)
0.6337578392589267 849
71.3%
-1.5778897522898205 341
28.7%

Length

2024-05-30T17:23:26.893801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:27.016762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.6337578392589267 849
71.3%
1.5778897522898205 341
28.7%

Most occurring characters

ValueCountFrequency (%)
7 3570
16.4%
8 3062
14.1%
5 2721
12.5%
2 2721
12.5%
3 2547
11.7%
9 2380
10.9%
6 1698
7.8%
0 1190
 
5.5%
. 1190
 
5.5%
- 341
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 3570
16.4%
8 3062
14.1%
5 2721
12.5%
2 2721
12.5%
3 2547
11.7%
9 2380
10.9%
6 1698
7.8%
0 1190
 
5.5%
. 1190
 
5.5%
- 341
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 3570
16.4%
8 3062
14.1%
5 2721
12.5%
2 2721
12.5%
3 2547
11.7%
9 2380
10.9%
6 1698
7.8%
0 1190
 
5.5%
. 1190
 
5.5%
- 341
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 3570
16.4%
8 3062
14.1%
5 2721
12.5%
2 2721
12.5%
3 2547
11.7%
9 2380
10.9%
6 1698
7.8%
0 1190
 
5.5%
. 1190
 
5.5%
- 341
 
1.6%

ASP_S_n
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size87.5 KiB
0.5378420829346794
923 
-1.8592818072985362
267 

Length

Max length19
Median length18
Mean length18.22437
Min length18

Characters and Unicode

Total characters21687
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5378420829346794
2nd row0.5378420829346794
3rd row0.5378420829346794
4th row0.5378420829346794
5th row0.5378420829346794

Common Values

ValueCountFrequency (%)
0.5378420829346794 923
77.6%
-1.8592818072985362 267
 
22.4%

Length

2024-05-30T17:23:27.155282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:27.283276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.5378420829346794 923
77.6%
1.8592818072985362 267
 
22.4%

Most occurring characters

ValueCountFrequency (%)
8 2914
13.4%
4 2769
12.8%
2 2647
12.2%
9 2380
11.0%
0 2113
9.7%
3 2113
9.7%
7 2113
9.7%
5 1457
6.7%
. 1190
5.5%
6 1190
5.5%
Other values (2) 801
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 2914
13.4%
4 2769
12.8%
2 2647
12.2%
9 2380
11.0%
0 2113
9.7%
3 2113
9.7%
7 2113
9.7%
5 1457
6.7%
. 1190
5.5%
6 1190
5.5%
Other values (2) 801
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 2914
13.4%
4 2769
12.8%
2 2647
12.2%
9 2380
11.0%
0 2113
9.7%
3 2113
9.7%
7 2113
9.7%
5 1457
6.7%
. 1190
5.5%
6 1190
5.5%
Other values (2) 801
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 2914
13.4%
4 2769
12.8%
2 2647
12.2%
9 2380
11.0%
0 2113
9.7%
3 2113
9.7%
7 2113
9.7%
5 1457
6.7%
. 1190
5.5%
6 1190
5.5%
Other values (2) 801
 
3.7%

TIKL_S_n
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size89.5 KiB
-0.14648968382726194
1165 
6.826419266350406
 
25

Length

Max length20
Median length20
Mean length19.936975
Min length17

Characters and Unicode

Total characters23725
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.14648968382726194
2nd row-0.14648968382726194
3rd row-0.14648968382726194
4th row-0.14648968382726194
5th row-0.14648968382726194

Common Values

ValueCountFrequency (%)
-0.14648968382726194 1165
97.9%
6.826419266350406 25
 
2.1%

Length

2024-05-30T17:23:27.432407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:27.573260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.14648968382726194 1165
97.9%
6.826419266350406 25
 
2.1%

Most occurring characters

ValueCountFrequency (%)
6 3620
15.3%
4 3545
14.9%
8 3520
14.8%
2 2380
10.0%
1 2355
9.9%
9 2355
9.9%
0 1215
 
5.1%
. 1190
 
5.0%
3 1190
 
5.0%
- 1165
 
4.9%
Other values (2) 1190
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 3620
15.3%
4 3545
14.9%
8 3520
14.8%
2 2380
10.0%
1 2355
9.9%
9 2355
9.9%
0 1215
 
5.1%
. 1190
 
5.0%
3 1190
 
5.0%
- 1165
 
4.9%
Other values (2) 1190
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 3620
15.3%
4 3545
14.9%
8 3520
14.8%
2 2380
10.0%
1 2355
9.9%
9 2355
9.9%
0 1215
 
5.1%
. 1190
 
5.0%
3 1190
 
5.0%
- 1165
 
4.9%
Other values (2) 1190
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 3620
15.3%
4 3545
14.9%
8 3520
14.8%
2 2380
10.0%
1 2355
9.9%
9 2355
9.9%
0 1215
 
5.1%
. 1190
 
5.0%
3 1190
 
5.0%
- 1165
 
4.9%
Other values (2) 1190
 
5.0%

TRENT_S_n
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size88.2 KiB
-0.5261406982307717
932 
1.9006322897328651
258 

Length

Max length19
Median length19
Mean length18.783193
Min length18

Characters and Unicode

Total characters22352
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.5261406982307717
2nd row1.9006322897328651
3rd row-0.5261406982307717
4th row-0.5261406982307717
5th row1.9006322897328651

Common Values

ValueCountFrequency (%)
-0.5261406982307717 932
78.3%
1.9006322897328651 258
 
21.7%

Length

2024-05-30T17:23:27.715763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T17:23:27.841402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.5261406982307717 932
78.3%
1.9006322897328651 258
 
21.7%

Most occurring characters

ValueCountFrequency (%)
0 3312
14.8%
7 3054
13.7%
2 2638
11.8%
6 2380
10.6%
1 2380
10.6%
9 1448
6.5%
8 1448
6.5%
3 1448
6.5%
. 1190
 
5.3%
5 1190
 
5.3%
Other values (2) 1864
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3312
14.8%
7 3054
13.7%
2 2638
11.8%
6 2380
10.6%
1 2380
10.6%
9 1448
6.5%
8 1448
6.5%
3 1448
6.5%
. 1190
 
5.3%
5 1190
 
5.3%
Other values (2) 1864
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3312
14.8%
7 3054
13.7%
2 2638
11.8%
6 2380
10.6%
1 2380
10.6%
9 1448
6.5%
8 1448
6.5%
3 1448
6.5%
. 1190
 
5.3%
5 1190
 
5.3%
Other values (2) 1864
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3312
14.8%
7 3054
13.7%
2 2638
11.8%
6 2380
10.6%
1 2380
10.6%
9 1448
6.5%
8 1448
6.5%
3 1448
6.5%
. 1190
 
5.3%
5 1190
 
5.3%
Other values (2) 1864
8.3%

Interactions

2024-05-30T17:22:59.610310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:58.194126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:58.673065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:59.144980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:59.728929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:58.317033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:58.783564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:59.261176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:59.851073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:58.428731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:58.900120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:59.374723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:59.961888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:58.546646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:59.021402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T17:22:59.484441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-30T17:23:28.060205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AGEANT_CA_S_nASP_S_nB_BLOK_S_nFIB_G_POSTFK_STENOKGBGEPAR_S_nGT_POSTIBS_POSTIM_PG_PINF_ANAMK_SH_POSTLID_S_nL_BLOODMP_TP_POSTNA_R_1_nNITR_SNOT_NA_1_nO_L_POSTSEXSIM_GIPERTSTENOK_ANSVT_POSTTIKL_S_nTIME_B_STRENT_S_nant_imendocr_01endocr_02fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08inf_imlat_imn_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10np_01np_04np_05np_07np_08np_09np_10nr_01nr_02nr_03nr_04nr_07nr_08nr_11zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06
AGE1.0000.0720.0000.1650.0870.1110.1070.1020.0850.1460.0000.0800.0000.000-0.0270.0820.0000.0000.0000.0940.3840.1170.1870.0000.0370.0250.0000.0000.1700.0770.0000.0360.1930.0000.0950.0760.0000.0120.0650.0000.0100.0000.0290.0250.0000.0600.1020.0860.0170.1210.1250.0000.0070.0000.0780.0780.0620.0000.0330.0000.0000.1130.0000.0000.0650.0000.0000.0000.0000.0540.0000.0000.0500.0930.0440.0190.0190.000
ANT_CA_S_n0.0721.0000.0000.1900.0220.0000.0900.0710.0000.0460.0110.0000.0000.0320.0140.0000.0550.0000.0000.0000.0420.0000.0230.0370.0000.0050.0000.0590.0670.0000.0690.0000.0210.0000.0000.0000.0000.0000.0530.0000.0570.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0180.0000.0000.000
ASP_S_n0.0000.0001.0000.0000.0000.1190.0930.1940.0000.0030.0000.0000.0000.037-0.0690.0200.0000.0000.0000.0000.0000.000-0.0290.0150.1220.0310.2460.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0420.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0150.0000.0160.000
B_BLOK_S_n0.1650.1900.0001.0000.0000.0580.0150.0080.0000.0480.0000.0000.0000.000-0.0460.0190.0000.0000.0000.0340.0380.062-0.0560.0000.0000.0840.0170.1300.0680.0000.0770.0000.0000.0000.0000.0000.0000.1410.1170.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0320.0000.0290.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0340.0290.0000.0000.000
FIB_G_POST0.0870.0220.0000.0001.0000.0290.0430.0000.0000.0320.0000.0000.0000.0550.0680.1130.0600.0000.0000.0000.0000.000-0.0510.0000.000-0.0050.0000.0410.0000.0000.0000.0000.0000.0000.0660.0000.0000.0750.0000.0000.1310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
FK_STENOK0.1110.0000.1190.0580.0291.0000.0610.0430.0310.5360.0290.1960.0000.075-0.0410.0110.0000.0000.0000.0440.0920.0000.8170.0470.0000.0230.0000.0580.1010.0000.0560.0340.0630.0000.0000.0000.0000.0430.0310.0000.0000.1150.0000.0550.0310.1010.0000.0000.0580.0000.0000.0000.0680.0570.0230.0830.0000.0000.0910.0000.0000.0000.0000.0000.2040.0000.1150.0610.0000.0450.0000.0000.1260.0000.1040.0000.0000.000
GB0.1070.0900.0930.0150.0430.0611.0000.0000.0300.0950.0620.0550.0340.059-0.0210.0000.0420.0360.0000.0780.2380.2260.1120.0230.0300.0460.0000.0520.0700.0500.0000.0000.0600.0000.0000.0000.0000.0130.0750.0000.1240.0000.0000.0180.1380.0690.0000.0830.0110.0000.0000.0000.0000.0450.0520.0000.0000.0000.0340.0000.0000.0150.4040.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0430.0960.0000.000
GEPAR_S_n0.1020.0710.1940.0080.0000.0430.0001.0000.0100.0760.0120.0000.0300.0700.0440.0000.1110.0000.0000.0000.0440.035-0.0020.0370.000-0.1320.0000.0610.0000.0000.0380.0140.0810.0000.0140.0000.0000.0480.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0180.0000.0000.0000.000
GT_POST0.0850.0000.0000.0000.0000.0310.0300.0101.0000.0210.0000.0350.1530.0530.0230.0000.0000.0000.0000.0000.0000.0000.0470.0000.000-0.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.2200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0450.0000.0000.0000.0000.0000.0000.0000.000
IBS_POST0.1460.0460.0030.0480.0320.5360.0950.0760.0211.0000.0310.2110.0000.092-0.0960.0420.0310.0000.0000.0000.0850.0000.2890.0320.0360.0680.0770.0840.0790.0000.0490.0470.1120.0160.0000.0360.0000.1000.0620.0000.0090.0000.0000.0770.0510.0220.0300.0000.0000.0000.0000.0000.0800.0000.0000.0880.0000.0080.0000.0080.0420.0000.0000.0000.0000.0000.0000.0000.0210.0730.0230.0000.0640.0250.0380.0000.0240.000
IM_PG_P0.0000.0110.0000.0000.0000.0290.0620.0120.0000.0311.0000.0000.0000.0000.0750.1250.1270.0900.0000.0000.0000.000-0.0130.0000.000-0.0690.0000.0640.0000.0000.0000.0140.0000.0000.0000.0230.0000.0880.0680.0000.0000.0000.0000.0940.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0180.0000.0000.0000.0610.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
INF_ANAM0.0800.0000.0000.0000.0000.1960.0550.0000.0350.2110.0001.0000.0000.096-0.0240.0000.0790.1150.0000.1240.0230.0000.3380.0060.000-0.0640.0000.0760.1100.0420.0440.0000.0650.0000.0000.0000.0000.0000.0610.0000.0580.0420.0000.0330.0830.0000.0520.0520.0000.0360.0000.0000.0000.0100.0000.0000.0000.0200.0000.0200.0570.0720.0200.0150.1310.0000.0550.0900.0450.0130.1280.0570.0420.0060.0000.0060.0000.000
K_SH_POST0.0000.0000.0000.0000.0000.0000.0340.0300.1530.0000.0000.0001.0000.0000.0460.0000.0000.0000.0000.0000.0220.0000.0120.0000.000-0.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0000.000
LID_S_n0.0000.0320.0370.0000.0550.0750.0590.0700.0530.0920.0000.0960.0001.0000.0770.0000.2500.0410.0640.0000.0920.000-0.0720.0120.000-0.1560.0000.1970.0570.0000.0190.0000.1050.0550.0380.0130.0000.1060.1280.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0240.0000.0130.2090.1550.0000.0000.0000.0000.0320.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0840.0000.0000.000
L_BLOOD-0.0270.014-0.069-0.0460.068-0.041-0.0210.0440.023-0.0960.075-0.0240.0460.0771.0000.0000.0890.1210.0000.0640.0000.000-0.0420.0390.000-0.1280.0840.0610.0000.0000.0000.0640.0640.0300.0000.0710.0000.0000.0810.0000.0000.0410.0740.0230.0000.0000.0500.0000.0620.0000.0350.0580.0350.0580.0000.0640.0930.0890.0000.0000.0000.0770.0000.0000.0340.0000.0990.0000.0000.0820.0000.0300.0750.0000.0740.0290.0480.000
MP_TP_POST0.0820.0000.0200.0190.1130.0110.0000.0000.0000.0420.1250.0000.0000.0000.0001.0000.0000.0160.0000.0560.0410.0000.0570.0000.000-0.0200.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0410.0410.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0450.0000.4190.4520.0000.0000.0290.0000.0000.0870.0000.0000.0000.0000.0000.0000.2010.4480.0000.0000.0930.0000.0000.0160.0000.000
NA_R_1_n0.0000.0550.0000.0000.0600.0000.0420.1110.0000.0310.1270.0790.0000.2500.0890.0001.0000.1730.0660.0000.0000.0000.0500.0000.000-0.2030.0480.1050.0580.0510.0450.1060.0830.1290.1560.0100.0000.0830.0790.0590.0000.0000.0000.0000.0000.0000.0520.0000.0000.1270.0000.0000.0760.0570.0400.0000.0000.1030.0680.0000.0000.0000.0000.0230.1460.0000.0000.0180.0000.0000.0000.0000.0370.0400.0900.0000.0000.000
NITR_S0.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0900.1150.0000.0410.1210.0160.1731.0000.0000.3490.0530.0000.0420.0000.000-0.0150.0360.0680.1190.0430.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0280.0320.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0100.0000.0460.0000.0000.0000.0200.0000.0850.0000.0000.000
NOT_NA_1_n0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0660.0001.0000.0400.0890.0000.0070.0210.000-0.0870.0470.0520.0630.0000.0660.0000.0000.0720.0000.0000.0000.0240.0400.0570.0000.0000.0000.0600.0470.0000.0230.0220.0270.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0190.0000.0000.0000.0000.0000.0000.022
O_L_POST0.0940.0000.0000.0340.0000.0440.0780.0000.0000.0000.0000.1240.0000.0000.0640.0560.0000.3490.0401.0000.0740.0000.0190.0660.0000.0060.0000.0000.0790.0000.0000.0000.0070.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0110.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0110.0750.0000.0000.0000.0000.0000.0490.0000.0000.000
SEX0.3840.0420.0000.0380.0000.0920.2380.0440.0000.0850.0000.0230.0220.0920.0000.0410.0000.0530.0890.0741.0000.058-0.0770.0000.021-0.0250.0850.0330.2480.0900.0210.0000.0770.0000.0000.0140.0000.0460.0470.0220.0000.0220.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0190.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.000
SIM_GIPERT0.1170.0000.0000.0620.0000.0000.2260.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0581.000-0.0200.0000.0000.0360.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.000
STENOK_AN0.1870.023-0.029-0.056-0.0510.8170.112-0.0020.0470.289-0.0130.3380.012-0.072-0.0420.0570.0500.0420.0070.019-0.077-0.0201.0000.0680.0120.0250.0600.0640.0810.0410.0000.0000.0780.0300.0000.0000.0000.0540.0490.0000.0830.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0550.0720.0000.0960.0310.0000.0620.0500.0630.0340.0000.0000.0000.0000.0220.0000.0330.0870.0000.0000.1080.0000.0420.0000.0000.000
SVT_POST0.0000.0370.0150.0000.0000.0470.0230.0370.0000.0320.0000.0060.0000.0120.0390.0000.0000.0000.0210.0660.0000.0000.0681.0000.000-0.0170.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0980.0000.0000.1430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1630.0000.0000.0370.0000.000
TIKL_S_n0.0370.0000.1220.0000.0000.0000.0300.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0120.0001.000-0.0010.0660.0000.0000.0000.0000.0000.0780.0000.0000.0210.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0780.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
TIME_B_S0.0250.0050.0310.084-0.0050.0230.046-0.132-0.0120.068-0.069-0.064-0.024-0.156-0.128-0.020-0.203-0.015-0.0870.006-0.0250.0360.025-0.017-0.0011.0000.0740.0530.0620.0350.0230.0340.1340.0000.0850.0000.0220.0470.0470.0000.0440.0840.0000.0560.0870.0370.0000.0100.0000.0450.0000.0400.0000.0860.0000.0000.0000.0750.0000.0220.1650.0000.0000.0000.0000.0330.0570.0760.0000.0000.0330.0750.0000.0000.0000.0000.0000.000
TRENT_S_n0.0000.0000.2460.0170.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0840.0000.0480.0360.0470.0000.0850.0000.0600.0000.0660.0741.0000.0000.0480.0000.0000.0000.0760.0000.0560.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0360.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.031
ant_im0.0000.0590.0000.1300.0410.0580.0520.0610.0000.0840.0640.0760.0000.1970.0610.0000.1050.0680.0520.0000.0330.0000.0640.0000.0000.0530.0001.0000.0580.0340.0000.0780.0350.0610.0000.0000.0000.3850.3900.0000.0330.1450.0000.0520.1380.0520.0610.0150.0000.0000.0780.0440.1040.0880.0000.0000.0000.0000.0000.0000.0000.0420.0000.0140.0000.0000.1060.0000.0000.0000.0000.0000.0900.0000.0000.0000.1140.072
endocr_010.1700.0670.0000.0680.0000.1010.0700.0000.0000.0790.0000.1100.0000.0570.0000.0000.0580.1190.0630.0790.2480.0680.0810.0080.0000.0620.0480.0581.0000.0720.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.000
endocr_020.0770.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0420.0000.0000.0000.0000.0510.0430.0000.0000.0900.0000.0410.0000.0000.0350.0000.0340.0721.0000.0000.0000.0000.0000.0000.0270.0000.0000.0060.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.000
fibr_ter_010.0000.0690.0000.0770.0000.0560.0000.0380.0000.0490.0000.0440.0000.0190.0000.0000.0450.0000.0660.0000.0210.0000.0000.0000.0000.0230.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.000
fibr_ter_020.0360.0000.0000.0000.0000.0340.0000.0140.0000.0470.0140.0000.0000.0000.0640.0000.1060.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0780.0000.0000.0001.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_030.1930.0210.0580.0000.0000.0630.0600.0810.0000.1120.0000.0650.0000.1050.0640.0090.0830.0000.0000.0070.0770.0000.0780.0000.0780.1340.0760.0350.0550.0000.0000.0001.0000.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0550.0300.0000.1290.0000.0720.0000.0000.0000.0300.0000.0000.0000.0000.0610.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.1220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_060.0950.0000.0000.0000.0660.0000.0000.0140.0000.0000.0000.0000.0000.0380.0000.0000.1560.0000.0000.0000.0000.0000.0000.0000.0000.0850.0560.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_070.0760.0000.0000.0000.0000.0000.0000.0000.0000.0360.0230.0000.0000.0130.0710.0000.0100.0000.0000.0000.0140.0000.0000.0000.0210.0000.0000.0000.0000.0270.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.000
inf_im0.0120.0000.0000.1410.0750.0430.0130.0480.0140.1000.0880.0000.0000.1060.0000.0410.0830.0600.0240.0000.0460.0000.0540.0980.0760.0470.0000.3850.0000.0000.0000.0320.0710.0000.0000.0000.0001.0000.3260.0000.0750.0000.0880.0950.0980.0000.0000.0510.0000.0000.1630.0310.0250.0060.0340.0360.0560.0470.1370.0470.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0870.0550.0000.0000.019
lat_im0.0650.0530.0000.1170.0000.0310.0750.0110.0080.0620.0680.0610.0730.1280.0810.0410.0790.0000.0400.0410.0470.0000.0490.0000.0000.0470.0210.3900.0000.0060.0290.0000.0000.1220.0000.0000.0000.3261.0000.0000.0000.0240.0000.0750.1190.0000.0000.0240.0000.0410.0900.0000.0240.0380.0350.0000.0000.0000.0240.0000.0000.0520.0000.0460.0000.0000.0000.0700.0000.0000.0000.0610.0000.0000.0430.0290.0320.000
n_p_ecg_p_010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0570.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_p_ecg_p_030.0100.0570.0000.0000.1310.0000.1240.0000.0000.0090.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.1430.0000.0440.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0850.0000.0000.0000.0000.000
n_p_ecg_p_040.0000.0000.0000.0000.0000.1150.0000.0000.0000.0000.0000.0420.0000.0000.0410.0000.0000.0000.0000.0000.0220.0410.0000.0000.0000.0840.0000.1450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.2000.000
n_p_ecg_p_050.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.000
n_p_ecg_p_060.0250.0000.0000.0000.0000.0550.0180.0000.0000.0770.0940.0330.0000.0000.0230.0000.0000.0280.0600.0000.0000.0000.0000.0000.0000.0560.0000.0520.0420.0000.0000.0000.0000.0000.0000.0000.0000.0950.0750.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.000
n_p_ecg_p_070.0000.0000.0000.0000.0000.0310.1380.0000.0000.0510.0000.0830.0000.0610.0000.0000.0000.0320.0470.0000.0000.0000.0560.0000.0000.0870.0000.1380.0000.0000.0000.0000.0230.0000.0000.0000.0000.0980.1190.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0200.0000.0000.0410.0400.0000.0000.0000.0000.0000.0000.0000.0710.0000.0000.0000.0000.0000.0000.0210.0000.0470.0180.0000.0000.0230.0000.0000.000
n_p_ecg_p_080.0600.0000.0000.0000.0000.1010.0690.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1720.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.041
n_p_ecg_p_090.1020.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0520.0000.0000.0500.0000.0520.0280.0230.0500.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_p_ecg_p_100.0860.0000.0000.0280.0000.0000.0830.0000.0000.0000.0000.0520.0000.0000.0000.0490.0000.0000.0220.0000.0750.0000.0000.0000.0000.0100.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0240.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0330.0540.0000.0000.0000.0000.0000.0000.0000.0980.1120.3360.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.000
n_p_ecg_p_110.0170.0110.0250.0000.0000.0580.0110.0090.0000.0000.0000.0000.0000.0000.0620.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0330.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0220.0000.0000.0000.000
n_p_ecg_p_120.1210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0360.0000.0240.0000.0000.1270.0000.0110.0000.0000.0000.0000.0000.0000.0450.0360.0000.0000.0000.0000.0000.0000.0000.0820.0000.0000.0000.0410.0000.0000.0000.0000.0000.0200.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0410.0000.0000.0000.1630.0000.0000.0000.0000.0000.0000.0410.0000.0000.0200.0000.0000.000
n_r_ecg_p_010.1250.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0780.0000.0000.0000.0000.0000.0000.0000.0000.0000.1630.0900.0390.0240.0000.0680.0000.0000.0000.0630.0000.0000.0001.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0390.0000.0000.0000.0000.0390.0000.0000.0000.0000.0220.000
n_r_ecg_p_020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_r_ecg_p_030.0070.0000.0000.0290.0000.0680.0000.0000.0000.0800.0000.0000.0000.2090.0350.0450.0760.0000.0000.0000.0000.0000.0550.0000.0000.0000.0450.1040.0000.0000.1110.0530.0910.0000.0000.0150.0000.0250.0240.0000.0000.0000.0000.0340.0410.0000.0000.0330.0330.0000.0600.0001.0000.0680.0460.0180.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0170.0100.0130.0000.0000.0740.0370.0000.0000.000
n_r_ecg_p_040.0000.0000.0000.0480.0000.0570.0450.0540.0000.0000.0000.0100.0000.1550.0580.0000.0570.0000.0000.0000.0190.0000.0720.0000.0320.0860.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0380.0000.0000.0000.0000.0000.0400.0000.0000.0540.0000.0000.0000.0000.0681.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.000
n_r_ecg_p_050.0780.0000.0000.0000.0000.0230.0520.0000.0000.0000.0180.0000.0000.0000.0000.4190.0400.0000.0000.0110.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0350.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0460.0001.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.2820.0000.0000.0000.0800.0000.0000.0000.0000.000
n_r_ecg_p_060.0780.0000.0420.0000.0000.0830.0000.0000.0390.0880.0000.0000.0790.0000.0640.4520.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0180.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.7650.0000.0000.0000.0000.0000.0000.0000.000
n_r_ecg_p_080.0620.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0930.0000.0000.0000.0000.1080.0000.0000.0310.5870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0660.0000.000
n_r_ecg_p_090.0000.0000.0000.0000.0000.0000.0000.0000.2200.0080.0000.0200.0000.0000.0890.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_r_ecg_p_100.0330.0000.0000.0000.0000.0910.0340.0000.0000.0000.0610.0000.0000.0320.0000.0290.0680.0100.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1370.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_040.0000.0310.0000.0000.0000.0000.0000.0000.0000.0420.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.1650.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2470.0000.0000.0000.0000.0000.000
np_050.1130.0000.0000.0000.0450.0000.0150.0000.0000.0000.0000.0720.0000.0190.0770.0870.0000.0000.0000.0000.0000.0000.0340.0000.0780.0000.0070.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0000.0000.0710.0000.0000.0980.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0110.0000.0000.1540.0000.0000.0000.0000.0000.0000.000
np_070.0000.0000.0000.0000.0000.0000.4040.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0460.0000.0000.0000.0000.0000.0000.0000.0000.3360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_090.0650.0000.0000.0000.0000.2040.0000.0000.0000.0000.0610.1310.0000.0000.0340.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1630.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.000
nr_010.0000.0000.0000.0000.0000.1150.0280.0000.0000.0000.0000.0550.0000.0000.0990.0000.0000.0100.0000.1390.0220.0000.0220.0000.0000.0570.0000.1060.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1720.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_020.0000.0000.0000.0000.0000.0610.0000.0370.0420.0000.0000.0900.0000.0000.0000.0000.0180.0000.0410.0110.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0700.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0450.0000.0000.0000.2010.0000.0460.0000.0750.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0170.0000.2820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_040.0540.0000.0330.0000.0000.0450.0000.0000.0450.0730.0000.0130.0860.0000.0820.4480.0000.0000.0000.0000.0000.0000.0870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0100.0000.0000.7650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_070.0000.0000.0000.0080.0000.0000.0000.0000.0000.0230.0000.1280.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.1540.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
nr_080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0410.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.2470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.2000.000
nr_110.0500.0000.0000.0000.0000.1260.0000.0000.0000.0640.0000.0420.0000.0000.0750.0930.0370.0200.0000.0000.0000.0000.1080.1630.0000.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0850.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0800.0000.1390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0430.0000.000
zab_leg_010.0930.0250.0000.0340.0000.0000.0000.0180.0000.0250.0000.0060.0000.0040.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0001.0000.0640.0000.0000.014
zab_leg_020.0440.0180.0150.0290.0000.1040.0430.0000.0000.0380.0000.0000.0000.0840.0740.0000.0900.0850.0000.0490.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0550.0430.0000.0000.0000.0530.0440.0230.0000.0000.0000.0000.0200.0000.0000.0370.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0641.0000.0000.0000.000
zab_leg_030.0190.0000.0000.0000.0000.0000.0960.0000.0000.0000.0000.0060.0000.0000.0290.0160.0000.0000.0000.0000.0080.0000.0000.0370.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0001.0000.0000.000
zab_leg_040.0190.0000.0160.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.1140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.2000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2000.0000.0000.0000.0001.0000.000
zab_leg_060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0310.0720.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0001.000

Missing values

2024-05-30T17:23:00.376993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-30T17:23:01.022072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AGESEXINF_ANAMSTENOK_ANFK_STENOKIBS_POSTGBSIM_GIPERTZSN_Anr_11nr_01nr_02nr_03nr_04nr_07nr_08np_01np_04np_05np_07np_08np_09np_10endocr_01endocr_02endocr_03zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06O_L_POSTK_SH_POSTMP_TP_POSTSVT_POSTGT_POSTFIB_G_POSTant_imlat_iminf_impost_imIM_PG_Pn_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10n_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08L_BLOODTIME_B_SR_AB_1_nR_AB_2_nR_AB_3_nNITR_SNA_R_1_nNA_R_2_nNA_R_3_nNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nLID_S_nB_BLOK_S_nANT_CA_S_nGEPAR_S_nASP_S_nTIKL_S_nTRENT_S_n
01.4961410.7088902.003196-0.411275-0.1227601.0807991.520508-0.1840750.0-0.171499-0.04103-0.112987-0.13403-0.109109-0.029001-0.04103-0.029001-0.04103-0.092057-0.029001-0.050273-0.04103-0.029001-0.360700-0.1372430.0-0.337983-0.226355-0.109109-0.050273-0.127379-0.213762-0.04103-0.215903-0.071187-0.064957-0.076923-0.267289-1.126742-0.6924950.0-0.143468-0.188900-0.064957-0.386986-0.2072316.218253-0.116742-0.050273-0.029001-0.04103-0.04103-0.116742-0.04103-0.029001-0.082269-0.24618317.219175-0.082269-0.123929-0.137243-0.184075-0.092057-0.071187-0.191273-0.050273-0.071187-0.064957-0.029001-0.153281-0.2528930.00.00.0-0.264847-0.5875340.00.0-0.5619160.00.01.600898-0.404036-1.5844000.6337580.537842-0.14649-0.526141
1-0.4758080.7088900.677019-0.836485-1.077563-1.383091-1.256072-0.1840750.0-0.171499-0.04103-0.112987-0.13403-0.109109-0.029001-0.04103-0.029001-0.04103-0.092057-0.029001-0.050273-0.04103-0.029001-0.360700-0.1372430.0-0.337983-0.226355-0.109109-0.050273-0.127379-0.213762-0.04103-0.215903-0.071187-0.064957-0.0769231.5607240.308829-0.6924950.0-0.143468-0.188900-0.064957-0.3869864.825527-0.160817-0.116742-0.050273-0.029001-0.04103-0.04103-0.116742-0.04103-0.029001-0.082269-0.246183-0.058075-0.082269-0.123929-0.137243-0.184075-0.092057-0.071187-0.191273-0.050273-0.071187-0.064957-0.029001-0.223491-0.9368540.00.00.0-0.264847-0.5875340.00.01.1548480.00.01.600898-0.4040360.6311540.6337580.537842-0.146491.900632
2-0.7447100.708890-0.649158-0.836485-1.0775631.0807990.594981-0.1840750.0-0.171499-0.04103-0.112987-0.13403-0.109109-0.029001-0.04103-0.029001-0.04103-0.092057-0.029001-0.050273-0.04103-0.029001-0.360700-0.1372430.0-0.337983-0.226355-0.109109-0.050273-0.127379-0.213762-0.04103-0.215903-0.071187-0.064957-0.0769231.5607240.308829-0.6924950.0-0.143468-0.188900-0.0649572.584070-0.207231-0.160817-0.116742-0.050273-0.029001-0.04103-0.04103-0.116742-0.04103-0.029001-0.082269-0.246183-0.058075-0.082269-0.123929-0.137243-0.184075-0.092057-0.071187-0.191273-0.050273-0.071187-0.064957-0.0290010.829651-0.5948730.00.00.0-0.2648470.9558770.00.03.7299940.00.01.6008982.475025-1.5844000.6337580.537842-0.14649-0.526141
30.689435-1.410656-0.649158-0.836485-1.0775631.0807990.594981-0.1840750.0-0.171499-0.04103-0.112987-0.13403-0.109109-0.029001-0.04103-0.029001-0.04103-0.092057-0.029001-0.050273-0.04103-0.029001-0.360700-0.1372430.02.958733-0.226355-0.109109-0.050273-0.127379-0.213762-0.04103-0.215903-0.071187-0.064957-0.076923-0.8766270.3088290.0539440.0-0.143468-0.188900-0.064957-0.386986-0.207231-0.160817-0.116742-0.050273-0.029001-0.04103-0.04103-0.116742-0.04103-0.029001-0.082269-0.246183-0.058075-0.082269-0.123929-0.137243-0.184075-0.092057-0.071187-0.191273-0.050273-0.071187-0.064957-0.0290010.000000-0.9368540.00.00.0-0.264847-0.5875340.00.0-0.5619160.00.0-0.624649-0.4040360.6311540.6337580.537842-0.14649-0.526141
4-0.0276380.708890-0.649158-0.836485-1.0775631.0807991.520508-0.1840750.0-0.171499-0.04103-0.112987-0.13403-0.109109-0.029001-0.04103-0.029001-0.04103-0.092057-0.029001-0.050273-0.04103-0.029001-0.360700-0.1372430.0-0.337983-0.226355-0.109109-0.050273-0.127379-0.213762-0.04103-0.215903-0.071187-0.064957-0.0769231.5607240.308829-0.6924950.0-0.143468-0.188900-0.064957-0.386986-0.207231-0.160817-0.116742-0.050273-0.029001-0.04103-0.04103-0.116742-0.04103-0.029001-0.082269-0.246183-0.058075-0.082269-0.123929-0.137243-0.184075-0.092057-0.071187-0.191273-0.050273-0.071187-0.064957-0.029001-0.0479671.4570080.00.00.0-0.264847-0.5875340.00.0-0.5619160.00.0-0.624649-0.4040360.631154-1.5778900.537842-0.146491.900632
50.3308980.708890-0.649158-0.4112750.832042-0.151146-1.256072-0.1840750.0-0.171499-0.04103-0.112987-0.13403-0.109109-0.029001-0.04103-0.029001-0.04103-0.092057-0.029001-0.050273-0.04103-0.029001-0.360700-0.1372430.0-0.337983-0.226355-0.109109-0.050273-0.127379-0.213762-0.04103-0.215903-0.071187-0.064957-0.076923-0.2672890.308829-0.6924950.0-0.143468-0.188900-0.064957-0.386986-0.207231-0.160817-0.116742-0.050273-0.029001-0.04103-0.04103-0.116742-0.04103-0.029001-0.082269-0.246183-0.058075-0.082269-0.123929-0.137243-0.184075-0.092057-0.071187-0.191273-0.050273-0.071187-0.064957-0.029001-0.434119-0.9368540.00.00.0-0.264847-0.5875340.00.0-0.5619160.00.0-0.6246492.475025-1.5844000.6337580.537842-0.14649-0.526141
60.8687030.7088900.677019-0.4112750.832042-0.1511460.594981-0.1840750.0-0.171499-0.04103-0.112987-0.13403-0.109109-0.029001-0.04103-0.029001-0.04103-0.092057-0.029001-0.050273-0.04103-0.029001-0.360700-0.1372430.02.958733-0.226355-0.109109-0.050273-0.127379-0.213762-0.04103-0.215903-0.071187-0.064957-0.076923-0.876627-1.1267421.5468240.0-0.143468-0.188900-0.064957-0.386986-0.207231-0.160817-0.116742-0.050273-0.029001-0.04103-0.04103-0.116742-0.04103-0.029001-0.082269-0.246183-0.058075-0.082269-0.123929-0.137243-0.184075-0.092057-0.071187-0.191273-0.050273-0.071187-0.064957-0.0290010.934965-1.2788340.00.00.0-0.264847-0.5875340.00.0-0.5619160.00.0-0.624649-0.404036-1.5844000.6337580.537842-0.146491.900632
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